{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/image-super-resolution/papers/3","list_of":"/task/image-super-resolution","task":"Image Super-Resolution","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":16,"rows_per_page":100,"rows":[201,300],"of":1589,"counts":{"archive_papers_tagged":1589,"with_a_code_link":783,"where_syntology_ran_a_sample":188,"not_listed_spam_title":0,"listed":1589,"listed_where_code_ran":188,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":171,"every_run_a_failure_of_syntologys_instrument":17,"listed_with_a_run_with_no_instrument_failure":171,"listed_every_run_a_failure_of_syntologys_instrument":17,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/image-super-resolution","prev":"/task/image-super-resolution/papers/2","next":"/task/image-super-resolution/papers/4","papers":[{"url":"/paper/exploring-semantic-feature-discrimination-for","slug":"exploring-semantic-feature-discrimination-for","title":"Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality Assessment","date":"2025-03-25","arxiv_id":"2503.19295","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exploring-semantic-feature-discrimination-for#ran","syntology_url":"https://syntology.ai/paper/2503.19295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.19295"}},"official":{"repos":["GuangluDong0728/SFD"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-guided-global-local-collaborative","slug":"semantic-guided-global-local-collaborative","title":"Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-Resolution","date":"2025-03-20","arxiv_id":"2503.16056","repositories_listed":1,"syntology":null},{"url":"/paper/rainscalegan-a-conditional-generative","slug":"rainscalegan-a-conditional-generative","title":"RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling","date":"2025-03-17","arxiv_id":"2503.13316","repositories_listed":1,"syntology":null},{"url":"/paper/qdm-quadtree-based-region-adaptive-sparse","slug":"qdm-quadtree-based-region-adaptive-sparse","title":"QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-Resolution","date":"2025-03-15","arxiv_id":"2503.12015","repositories_listed":1,"syntology":null},{"url":"/paper/perceive-understand-and-restore-real-world","slug":"perceive-understand-and-restore-real-world","title":"Perceive, Understand and Restore: Real-World Image Super-Resolution with Autoregressive Multimodal Generative Models","date":"2025-03-14","arxiv_id":"2503.11073","repositories_listed":1,"syntology":null},{"url":"/paper/megasr-mining-customized-semantics-and","slug":"megasr-mining-customized-semantics-and","title":"MegaSR: Mining Customized Semantics and Expressive Guidance for Image Super-Resolution","date":"2025-03-11","arxiv_id":"2503.08096","repositories_listed":1,"syntology":null},{"url":"/paper/catanet-efficient-content-aware-token","slug":"catanet-efficient-content-aware-token","title":"CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution","date":"2025-03-10","arxiv_id":"2503.06896","repositories_listed":1,"syntology":null},{"url":"/paper/emulating-self-attention-with-convolution-for","slug":"emulating-self-attention-with-convolution-for","title":"Emulating Self-attention with Convolution for Efficient Image Super-Resolution","date":"2025-03-09","arxiv_id":"2503.06671","repositories_listed":1,"syntology":null},{"url":"/paper/qartsr-quantization-via-reverse-module-and","slug":"qartsr-quantization-via-reverse-module-and","title":"QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution","date":"2025-03-07","arxiv_id":"2503.05584","repositories_listed":1,"syntology":null},{"url":"/paper/autolut-lut-based-image-super-resolution-with","slug":"autolut-lut-based-image-super-resolution-with","title":"AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual Learning","date":"2025-03-03","arxiv_id":"2503.01565","repositories_listed":1,"syntology":null},{"url":"/paper/difiisr-a-diffusion-model-with-gradient","slug":"difiisr-a-diffusion-model-with-gradient","title":"DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution","date":"2025-03-03","arxiv_id":"2503.01187","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/difiisr-a-diffusion-model-with-gradient#ran","syntology_url":"https://syntology.ai/paper/2503.01187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.01187"}},"official":{"repos":["zirui0625/difiisr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/badrefsr-backdoor-attacks-against-reference","slug":"badrefsr-backdoor-attacks-against-reference","title":"BadRefSR: Backdoor Attacks Against Reference-based Image Super Resolution","date":"2025-02-28","arxiv_id":"2502.20943","repositories_listed":1,"syntology":null},{"url":"/paper/condiquant-condition-number-based-low-bit","slug":"condiquant-condition-number-based-low-bit","title":"CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution","date":"2025-02-21","arxiv_id":"2502.15478","repositories_listed":1,"syntology":null},{"url":"/paper/data-driven-super-resolution-of-flood","slug":"data-driven-super-resolution-of-flood","title":"Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations","date":"2025-02-14","arxiv_id":"2502.10601","repositories_listed":1,"syntology":null},{"url":"/paper/heterogeneous-mixture-of-experts-for-remote","slug":"heterogeneous-mixture-of-experts-for-remote","title":"Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution","date":"2025-02-12","arxiv_id":"2502.09654","repositories_listed":1,"syntology":null},{"url":"/paper/fast-omni-directional-image-super-resolution","slug":"fast-omni-directional-image-super-resolution","title":"Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric Priors","date":"2025-02-09","arxiv_id":"2502.05902","repositories_listed":1,"syntology":null},{"url":"/paper/one-diffusion-step-to-real-world-super","slug":"one-diffusion-step-to-real-world-super","title":"One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation","date":"2025-02-04","arxiv_id":"2502.01993","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-linear-attention-alternative-for","slug":"exploring-linear-attention-alternative-for","title":"Exploring Linear Attention Alternative for Single Image Super-Resolution","date":"2025-02-01","arxiv_id":"2502.00404","repositories_listed":1,"syntology":null},{"url":"/paper/visual-autoregressive-modeling-for-image","slug":"visual-autoregressive-modeling-for-image","title":"Visual Autoregressive Modeling for Image Super-Resolution","date":"2025-01-31","arxiv_id":"2501.18993","repositories_listed":1,"syntology":{"n":22,"n_ran":16,"n_constructed":0,"n_ran_checked":13,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":7,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/visual-autoregressive-modeling-for-image#ran","syntology_url":"https://syntology.ai/paper/2501.18993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.18993"}},"official":{"repos":["qyp2000/varsr"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/hsrmamba-contextual-spatial-spectral-state","slug":"hsrmamba-contextual-spatial-spectral-state","title":"HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution","date":"2025-01-30","arxiv_id":"2501.18500","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-attention-sharing-information","slug":"efficient-attention-sharing-information","title":"Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-Resolution","date":"2025-01-27","arxiv_id":"2501.15774","repositories_listed":1,"syntology":null},{"url":"/paper/neurop-diff-continuous-remote-sensing-image","slug":"neurop-diff-continuous-remote-sensing-image","title":"NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator Diffusion","date":"2025-01-15","arxiv_id":"2501.09054","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":6,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":9,"phrase":"9 ran (of which 6 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neurop-diff-continuous-remote-sensing-image#ran","syntology_url":"https://syntology.ai/paper/2501.09054","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.09054"}},"official":{"repos":["zerono000/neurop-diff"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":6,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/structsr-refuse-spurious-details-in-real","slug":"structsr-refuse-spurious-details-in-real","title":"StructSR: Refuse Spurious Details in Real-World Image Super-Resolution","date":"2025-01-10","arxiv_id":"2501.05777","repositories_listed":1,"syntology":null},{"url":"/paper/deterministic-image-to-image-translation-via","slug":"deterministic-image-to-image-translation-via","title":"Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/volformer-explore-more-comprehensive-cube","slug":"volformer-explore-more-comprehensive-cube","title":"VolFormer: Explore More Comprehensive Cube Interaction for Hyperspectral Image Restoration and Beyond","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-image-restoration-using-few-step","slug":"zero-shot-image-restoration-using-few-step","title":"Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)","date":"2024-12-29","arxiv_id":"2412.20596","repositories_listed":1,"syntology":null},{"url":"/paper/mair-a-locality-and-continuity-preserving","slug":"mair-a-locality-and-continuity-preserving","title":"MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration","date":"2024-12-28","arxiv_id":"2412.20066","repositories_listed":1,"syntology":null},{"url":"/paper/plug-and-play-tri-branch-invertible-block-for","slug":"plug-and-play-tri-branch-invertible-block-for","title":"Plug-and-Play Tri-Branch Invertible Block for Image Rescaling","date":"2024-12-18","arxiv_id":"2412.13508","repositories_listed":1,"syntology":null},{"url":"/paper/arbitrary-steps-image-super-resolution-via","slug":"arbitrary-steps-image-super-resolution-via","title":"Arbitrary-steps Image Super-resolution via Diffusion Inversion","date":"2024-12-12","arxiv_id":"2412.09013","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/arbitrary-steps-image-super-resolution-via#ran","syntology_url":"https://syntology.ai/paper/2412.09013","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.09013"}},"official":{"repos":["zsyoaoa/invsr"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/oftsr-one-step-flow-for-image-super","slug":"oftsr-one-step-flow-for-image-super","title":"OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs","date":"2024-12-12","arxiv_id":"2412.09465","repositories_listed":1,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/oftsr-one-step-flow-for-image-super#ran","syntology_url":"https://syntology.ai/paper/2412.09465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.09465"}},"official":{"repos":["yuanzhi-zhu/oftsr"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/realosr-latent-unfolding-boosting-diffusion","slug":"realosr-latent-unfolding-boosting-diffusion","title":"RealOSR: Latent Unfolding Boosting Diffusion-based Real-world Omnidirectional Image Super-Resolution","date":"2024-12-11","arxiv_id":"2412.09646","repositories_listed":1,"syntology":null},{"url":"/paper/rap-sr-restoration-prior-enhancement-in","slug":"rap-sr-restoration-prior-enhancement-in","title":"RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic Image Super-Resolution","date":"2024-12-10","arxiv_id":"2412.07149","repositories_listed":1,"syntology":null},{"url":"/paper/rfsr-improving-isr-diffusion-models-via","slug":"rfsr-improving-isr-diffusion-models-via","title":"RFSR: Improving ISR Diffusion Models via Reward Feedback Learning","date":"2024-12-04","arxiv_id":"2412.03268","repositories_listed":1,"syntology":null},{"url":"/paper/tasr-timestep-aware-diffusion-model-for-image","slug":"tasr-timestep-aware-diffusion-model-for-image","title":"TASR: Timestep-Aware Diffusion Model for Image Super-Resolution","date":"2024-12-04","arxiv_id":"2412.03355","repositories_listed":1,"syntology":null},{"url":"/paper/auto-encoded-supervision-for-perceptual-image","slug":"auto-encoded-supervision-for-perceptual-image","title":"Auto-Encoded Supervision for Perceptual Image Super-Resolution","date":"2024-11-28","arxiv_id":"2412.00124","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/auto-encoded-supervision-for-perceptual-image#ran","syntology_url":"https://syntology.ai/paper/2412.00124","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.00124"}},"official":{"repos":["2minkyulee/aesop-auto-encoded-supervision-for-perceptual-image-super-resolution"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/holisdip-image-super-resolution-via-holistic","slug":"holisdip-image-super-resolution-via-holistic","title":"HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior","date":"2024-11-27","arxiv_id":"2411.18662","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/holisdip-image-super-resolution-via-holistic#ran","syntology_url":"https://syntology.ai/paper/2411.18662","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.18662"}},"official":{"repos":["liyuantsao/HoliSDiP"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tsd-sr-one-step-diffusion-with-target-score","slug":"tsd-sr-one-step-diffusion-with-target-score","title":"TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution","date":"2024-11-27","arxiv_id":"2411.18263","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tsd-sr-one-step-diffusion-with-target-score#ran","syntology_url":"https://syntology.ai/paper/2411.18263","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.18263"}},"official":{"repos":["Microtreei/TSD-SR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mat-multi-range-attention-transformer-for","slug":"mat-multi-range-attention-transformer-for","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","date":"2024-11-26","arxiv_id":"2411.17214","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mat-multi-range-attention-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2411.17214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.17214"}},"official":{"repos":["stella-von/MAT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/passionsr-post-training-quantization-with","slug":"passionsr-post-training-quantization-with","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","date":"2024-11-26","arxiv_id":"2411.17106","repositories_listed":1,"syntology":null},{"url":"/paper/contourlet-refinement-gate-framework-for","slug":"contourlet-refinement-gate-framework-for","title":"Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution","date":"2024-11-19","arxiv_id":"2411.12530","repositories_listed":1,"syntology":null},{"url":"/paper/zoomed-in-diffused-out-towards-local","slug":"zoomed-in-diffused-out-towards-local","title":"Zoomed In, Diffused Out: Towards Local Degradation-Aware Multi-Diffusion for Extreme Image Super-Resolution","date":"2024-11-18","arxiv_id":"2411.12072","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/zoomed-in-diffused-out-towards-local#ran","syntology_url":"https://syntology.ai/paper/2411.12072","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.12072"}},"official":{"repos":["Brian-Moser/zido"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-based-ckm-construction-with","slug":"deep-learning-based-ckm-construction-with","title":"Deep Learning-Based CKM Construction with Image Super-Resolution","date":"2024-10-28","arxiv_id":"2411.08887","repositories_listed":1,"syntology":null},{"url":"/paper/guidance-disentanglement-network-for-optics","slug":"guidance-disentanglement-network-for-optics","title":"Guidance Disentanglement Network for Optics-Guided Thermal UAV Image Super-Resolution","date":"2024-10-27","arxiv_id":"2410.20466","repositories_listed":1,"syntology":null},{"url":"/paper/sebica-lightweight-spatial-and-efficient","slug":"sebica-lightweight-spatial-and-efficient","title":"Sebica: Lightweight Spatial and Efficient Bidirectional Channel Attention Super Resolution Network","date":"2024-10-27","arxiv_id":"2410.20546","repositories_listed":1,"syntology":null},{"url":"/paper/clearsr-latent-low-resolution-image","slug":"clearsr-latent-low-resolution-image","title":"ControlSR: Taming Diffusion Models for Consistent Real-World Image Super Resolution","date":"2024-10-18","arxiv_id":"2410.14279","repositories_listed":1,"syntology":null},{"url":"/paper/hasn-hybrid-attention-separable-network-for","slug":"hasn-hybrid-attention-separable-network-for","title":"HASN: Hybrid Attention Separable Network for Efficient Image Super-resolution","date":"2024-10-13","arxiv_id":"2410.09844","repositories_listed":1,"syntology":null},{"url":"/paper/maskblur-spatial-and-angular-data","slug":"maskblur-spatial-and-angular-data","title":"MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution","date":"2024-10-09","arxiv_id":"2410.06478","repositories_listed":1,"syntology":null},{"url":"/paper/enhanced-super-resolution-training-via","slug":"enhanced-super-resolution-training-via","title":"Enhanced Super-Resolution Training via Mimicked Alignment for Real-World Scenes","date":"2024-10-07","arxiv_id":"2410.05410","repositories_listed":1,"syntology":null},{"url":"/paper/distillation-free-one-step-diffusion-for-real","slug":"distillation-free-one-step-diffusion-for-real","title":"Distillation-Free One-Step Diffusion for Real-World Image Super-Resolution","date":"2024-10-05","arxiv_id":"2410.04224","repositories_listed":1,"syntology":null},{"url":"/paper/posterior-mean-rectified-flow-towards-minimum","slug":"posterior-mean-rectified-flow-towards-minimum","title":"Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration","date":"2024-10-01","arxiv_id":"2410.00418","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/posterior-mean-rectified-flow-towards-minimum#ran","syntology_url":"https://syntology.ai/paper/2410.00418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.00418"}},"official":{"repos":["ohayonguy/PMRF"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/effective-diffusion-transformer-architecture","slug":"effective-diffusion-transformer-architecture","title":"Effective Diffusion Transformer Architecture for Image Super-Resolution","date":"2024-09-29","arxiv_id":"2409.19589","repositories_listed":1,"syntology":null},{"url":"/paper/unifying-dimensions-a-linear-adaptive","slug":"unifying-dimensions-a-linear-adaptive","title":"Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-Resolution","date":"2024-09-26","arxiv_id":"2409.17597","repositories_listed":1,"syntology":null},{"url":"/paper/degradation-guided-one-step-image-super","slug":"degradation-guided-one-step-image-super","title":"Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors","date":"2024-09-25","arxiv_id":"2409.17058","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/degradation-guided-one-step-image-super#ran","syntology_url":"https://syntology.ai/paper/2409.17058","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17058"}},"official":{"repos":["arctichare105/s3diff"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/thinking-in-granularity-dynamic-quantization","slug":"thinking-in-granularity-dynamic-quantization","title":"Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity Clues","date":"2024-09-22","arxiv_id":"2409.14330","repositories_listed":1,"syntology":null},{"url":"/paper/burstm-deep-burst-multi-scale-sr-using","slug":"burstm-deep-burst-multi-scale-sr-using","title":"BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical Flow","date":"2024-09-21","arxiv_id":"2409.15384","repositories_listed":1,"syntology":null},{"url":"/paper/wavemixsr-v2-enhancing-super-resolution-with","slug":"wavemixsr-v2-enhancing-super-resolution-with","title":"WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency","date":"2024-09-16","arxiv_id":"2409.10582","repositories_listed":1,"syntology":null},{"url":"/paper/eigensr-eigenimage-bridged-pre-trained-rgb","slug":"eigensr-eigenimage-bridged-pre-trained-rgb","title":"EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution","date":"2024-09-06","arxiv_id":"2409.04050","repositories_listed":1,"syntology":null},{"url":"/paper/lmlt-low-to-high-multi-level-vision","slug":"lmlt-low-to-high-multi-level-vision","title":"LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-Resolution","date":"2024-09-05","arxiv_id":"2409.03516","repositories_listed":1,"syntology":null},{"url":"/paper/perceptual-distortion-balanced-image-super","slug":"perceptual-distortion-balanced-image-super","title":"Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem","date":"2024-09-05","arxiv_id":"2409.03179","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-image-super-resolution-from","slug":"rethinking-image-super-resolution-from","title":"Rethinking Image Super-Resolution from Training Data Perspectives","date":"2024-09-01","arxiv_id":"2409.00768","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rethinking-image-super-resolution-from#ran","syntology_url":"https://syntology.ai/paper/2409.00768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.00768"}},"official":{"repos":["gohtanii/DiverSeg-dataset"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hitsr-a-hierarchical-transformer-for","slug":"hitsr-a-hierarchical-transformer-for","title":"HiTSR: A Hierarchical Transformer for Reference-based Super-Resolution","date":"2024-08-30","arxiv_id":"2408.16959","repositories_listed":1,"syntology":null},{"url":"/paper/ressr-a-residual-approach-to-super-resolving","slug":"ressr-a-residual-approach-to-super-resolving","title":"ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images","date":"2024-08-23","arxiv_id":"2408.13225","repositories_listed":1,"syntology":null},{"url":"/paper/mambacsr-dual-interleaved-scanning-for","slug":"mambacsr-dual-interleaved-scanning-for","title":"MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs","date":"2024-08-21","arxiv_id":"2408.11758","repositories_listed":1,"syntology":null},{"url":"/paper/implicit-grid-convolution-for-multi-scale","slug":"implicit-grid-convolution-for-multi-scale","title":"Implicit Grid Convolution for Multi-Scale Image Super-Resolution","date":"2024-08-19","arxiv_id":"2408.09674","repositories_listed":1,"syntology":null},{"url":"/paper/ml-craist-multi-scale-low-high-frequency","slug":"ml-craist-multi-scale-low-high-frequency","title":"ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer","date":"2024-08-19","arxiv_id":"2408.09940","repositories_listed":1,"syntology":null},{"url":"/paper/task-aware-dynamic-transformer-for-efficient","slug":"task-aware-dynamic-transformer-for-efficient","title":"Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution","date":"2024-08-16","arxiv_id":"2408.08736","repositories_listed":1,"syntology":null},{"url":"/paper/grformer-grouped-residual-self-attention-for","slug":"grformer-grouped-residual-self-attention-for","title":"GRFormer: Grouped Residual Self-Attention for Lightweight Single Image Super-Resolution","date":"2024-08-14","arxiv_id":"2408.07484","repositories_listed":1,"syntology":null},{"url":"/paper/one-step-diffusion-based-super-resolution","slug":"one-step-diffusion-based-super-resolution","title":"One Step Diffusion-based Super-Resolution with Time-Aware Distillation","date":"2024-08-14","arxiv_id":"2408.07476","repositories_listed":1,"syntology":null},{"url":"/paper/ssl-a-self-similarity-loss-for-improving","slug":"ssl-a-self-similarity-loss-for-improving","title":"SSL: A Self-similarity Loss for Improving Generative Image Super-resolution","date":"2024-08-11","arxiv_id":"2408.05713","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-dataset-and-framework-for-real-world","slug":"a-new-dataset-and-framework-for-real-world","title":"A New Dataset and Framework for Real-World Blurred Images Super-Resolution","date":"2024-07-20","arxiv_id":"2407.14880","repositories_listed":1,"syntology":null},{"url":"/paper/large-kernel-distillation-network-for","slug":"large-kernel-distillation-network-for","title":"Large Kernel Distillation Network for Efficient Single Image Super-Resolution","date":"2024-07-19","arxiv_id":"2407.14340","repositories_listed":1,"syntology":null},{"url":"/paper/realviformer-investigating-attention-for-real","slug":"realviformer-investigating-attention-for-real","title":"RealViformer: Investigating Attention for Real-World Video Super-Resolution","date":"2024-07-19","arxiv_id":"2407.13987","repositories_listed":1,"syntology":{"n":23,"n_ran":19,"n_constructed":0,"n_ran_checked":15,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":2,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/realviformer-investigating-attention-for-real#ran","syntology_url":"https://syntology.ai/paper/2407.13987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.13987"}},"official":{"repos":["yuehan717/realviformer"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/restore-rwkv-efficient-and-effective-medical","slug":"restore-rwkv-efficient-and-effective-medical","title":"Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV","date":"2024-07-14","arxiv_id":"2407.11087","repositories_listed":1,"syntology":null},{"url":"/paper/region-attention-transformer-for-medical","slug":"region-attention-transformer-for-medical","title":"Region Attention Transformer for Medical Image Restoration","date":"2024-07-12","arxiv_id":"2407.09268","repositories_listed":1,"syntology":null},{"url":"/paper/pairwise-distance-distillation-for","slug":"pairwise-distance-distillation-for","title":"Pairwise Distance Distillation for Unsupervised Real-World Image Super-Resolution","date":"2024-07-10","arxiv_id":"2407.07302","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/pairwise-distance-distillation-for#ran","syntology_url":"https://syntology.ai/paper/2407.07302","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07302"}},"official":{"repos":["yuehan717/pdd"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/anysr-realizing-image-super-resolution-as-any","slug":"anysr-realizing-image-super-resolution-as-any","title":"AnySR: Realizing Image Super-Resolution as Any-Scale, Any-Resource","date":"2024-07-05","arxiv_id":"2407.04241","repositories_listed":1,"syntology":null},{"url":"/paper/asteisr-adapting-single-image-super","slug":"asteisr-adapting-single-image-super","title":"ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolution","date":"2024-07-04","arxiv_id":"2407.03598","repositories_listed":1,"syntology":null},{"url":"/paper/preserving-full-degradation-details-for-blind","slug":"preserving-full-degradation-details-for-blind","title":"Preserving Full Degradation Details for Blind Image Super-Resolution","date":"2024-07-01","arxiv_id":"2407.01299","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-temporal-hierarchical-reinforcement","slug":"spatial-temporal-hierarchical-reinforcement","title":"Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution","date":"2024-06-26","arxiv_id":"2406.18310","repositories_listed":1,"syntology":null},{"url":"/paper/dalpsr-leverage-degradation-aligned-language","slug":"dalpsr-leverage-degradation-aligned-language","title":"DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution","date":"2024-06-24","arxiv_id":"2406.16477","repositories_listed":1,"syntology":null},{"url":"/paper/learning-accurate-and-enriched-features-for","slug":"learning-accurate-and-enriched-features-for","title":"Learning Accurate and Enriched Features for Stereo Image Super-Resolution","date":"2024-06-23","arxiv_id":"2406.16001","repositories_listed":1,"syntology":null},{"url":"/paper/ig-cfat-an-improved-gan-based-framework-for","slug":"ig-cfat-an-improved-gan-based-framework-for","title":"IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-Resolution","date":"2024-06-19","arxiv_id":"2406.13815","repositories_listed":1,"syntology":null},{"url":"/paper/a-dictionary-based-approach-for-removing-out","slug":"a-dictionary-based-approach-for-removing-out","title":"A Dictionary Based Approach for Removing Out-of-Focus Blur","date":"2024-06-17","arxiv_id":"2406.11330","repositories_listed":1,"syntology":null},{"url":"/paper/blind-super-resolution-via-meta-learning-and","slug":"blind-super-resolution-via-meta-learning-and","title":"Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation","date":"2024-06-13","arxiv_id":"2406.08896","repositories_listed":1,"syntology":null},{"url":"/paper/sr-caco-2-a-dataset-for-confocal-fluorescence","slug":"sr-caco-2-a-dataset-for-confocal-fluorescence","title":"SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution","date":"2024-06-13","arxiv_id":"2406.09168","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sr-caco-2-a-dataset-for-confocal-fluorescence#ran","syntology_url":"https://syntology.ai/paper/2406.09168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09168"}},"official":{"repos":["sbelharbi/sr-caco-2"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ddr-exploiting-deep-degradation-response-as","slug":"ddr-exploiting-deep-degradation-response-as","title":"DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor","date":"2024-06-12","arxiv_id":"2406.08377","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/ddr-exploiting-deep-degradation-response-as#ran","syntology_url":"https://syntology.ai/paper/2406.08377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.08377"}},"official":{"repos":["eezkni/ddr"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/one-step-effective-diffusion-network-for-real","slug":"one-step-effective-diffusion-network-for-real","title":"One-Step Effective Diffusion Network for Real-World Image Super-Resolution","date":"2024-06-12","arxiv_id":"2406.08177","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/one-step-effective-diffusion-network-for-real#ran","syntology_url":"https://syntology.ai/paper/2406.08177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.08177"}},"official":{"repos":["cswry/osediff"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/2dquant-low-bit-post-training-quantization","slug":"2dquant-low-bit-post-training-quantization","title":"2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution","date":"2024-06-10","arxiv_id":"2406.06649","repositories_listed":1,"syntology":null},{"url":"/paper/binarized-diffusion-model-for-image-super","slug":"binarized-diffusion-model-for-image-super","title":"Binarized Diffusion Model for Image Super-Resolution","date":"2024-06-09","arxiv_id":"2406.05723","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/binarized-diffusion-model-for-image-super#ran","syntology_url":"https://syntology.ai/paper/2406.05723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05723"}},"official":{"repos":["zhengchen1999/bi-diffsr"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/principled-probabilistic-imaging-using","slug":"principled-probabilistic-imaging-using","title":"Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors","date":"2024-05-29","arxiv_id":"2405.18782","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/principled-probabilistic-imaging-using#ran","syntology_url":"https://syntology.ai/paper/2405.18782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.18782"}},"official":{"repos":["zihuiwu/PnP-DM-public"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/single-image-super-resolution-based-on-1","slug":"single-image-super-resolution-based-on-1","title":"Single image super-resolution based on trainable feature matching attention network","date":"2024-05-29","arxiv_id":"2405.18872","repositories_listed":1,"syntology":null},{"url":"/paper/does-diffusion-beat-gan-in-image-super","slug":"does-diffusion-beat-gan-in-image-super","title":"Does Diffusion Beat GAN in Image Super Resolution?","date":"2024-05-27","arxiv_id":"2405.17261","repositories_listed":1,"syntology":null},{"url":"/paper/patchscaler-an-efficient-patch-independent","slug":"patchscaler-an-efficient-patch-independent","title":"PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-Resolution","date":"2024-05-27","arxiv_id":"2405.17158","repositories_listed":1,"syntology":null},{"url":"/paper/fast-denoising-diffusion-probabilistic-models","slug":"fast-denoising-diffusion-probabilistic-models","title":"Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation","date":"2024-05-23","arxiv_id":"2405.14802","repositories_listed":1,"syntology":null},{"url":"/paper/irsrmamba-infrared-image-super-resolution-via","slug":"irsrmamba-infrared-image-super-resolution-via","title":"IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model","date":"2024-05-16","arxiv_id":"2405.09873","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":5,"n_instrument":7,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 7 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/irsrmamba-infrared-image-super-resolution-via#ran","syntology_url":"https://syntology.ai/paper/2405.09873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.09873"}},"official":{"repos":["yongsongh/irsrmamba"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/nafrssr-a-lightweight-recursive-network-for","slug":"nafrssr-a-lightweight-recursive-network-for","title":"NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-Resolution","date":"2024-05-14","arxiv_id":"2405.08423","repositories_listed":1,"syntology":null},{"url":"/paper/cdformer-when-degradation-prediction-embraces","slug":"cdformer-when-degradation-prediction-embraces","title":"CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution","date":"2024-05-13","arxiv_id":"2405.07648","repositories_listed":1,"syntology":{"n":17,"n_ran":11,"n_constructed":7,"n_ran_checked":10,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":1,"phrase":"11 ran (of which 7 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/cdformer-when-degradation-prediction-embraces#ran","syntology_url":"https://syntology.ai/paper/2405.07648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.07648"}},"official":{"repos":["i2-multimedia-lab/cdformer"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":7,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/exploring-the-low-pass-filtering-behavior-in","slug":"exploring-the-low-pass-filtering-behavior-in","title":"Exploring the Low-Pass Filtering Behavior in Image Super-Resolution","date":"2024-05-13","arxiv_id":"2405.07919","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/exploring-the-low-pass-filtering-behavior-in#ran","syntology_url":"https://syntology.ai/paper/2405.07919","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.07919"}},"official":{"repos":["risingentropy/lpfinisr"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-real-world-image-super-resolution","slug":"efficient-real-world-image-super-resolution","title":"Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution","date":"2024-05-11","arxiv_id":"2405.07023","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-guided-large-scale-factor-remote","slug":"semantic-guided-large-scale-factor-remote","title":"Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior","date":"2024-05-11","arxiv_id":"2405.07044","repositories_listed":1,"syntology":null}],"record_sha256":"dd47640d116ea168b73a4b5f46914b1a3747ace6b78b0c5e5c22ea70866ff792","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}