{"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/deblurring/papers/5","list_of":"/task/deblurring","task":"Deblurring","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":5,"pages_in_order":10,"rows_per_page":100,"rows":[401,500],"of":999,"counts":{"archive_papers_tagged":999,"with_a_code_link":424,"where_syntology_ran_a_sample":115,"not_listed_spam_title":0,"listed":999,"listed_where_code_ran":115,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":103,"every_run_a_failure_of_syntologys_instrument":12,"listed_with_a_run_with_no_instrument_failure":103,"listed_every_run_a_failure_of_syntologys_instrument":12,"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/deblurring","prev":"/task/deblurring/papers/4","next":"/task/deblurring/papers/6","papers":[{"url":"/paper/deep-unfolding-of-a-proximal-interior-point","slug":"deep-unfolding-of-a-proximal-interior-point","title":"Deep Unfolding of a Proximal Interior Point Method for Image Restoration","date":"2018-12-11","arxiv_id":"1812.04276","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-deblurring-for-natural-imaging","slug":"convolutional-deblurring-for-natural-imaging","title":"Convolutional Deblurring for Natural Imaging","date":"2018-10-25","arxiv_id":"1810.10725","repositories_listed":1,"syntology":null},{"url":"/paper/fourier-domain-optimization-for-image","slug":"fourier-domain-optimization-for-image","title":"Fourier-Domain Optimization for Image Processing","date":"2018-09-11","arxiv_id":"1809.04187","repositories_listed":1,"syntology":null},{"url":"/paper/image-deblurring-with-a-class-specific-prior","slug":"image-deblurring-with-a-class-specific-prior","title":"Image Deblurring with a Class-Specific Prior","date":"2018-07-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-scene-deblurring-using-spatially","slug":"dynamic-scene-deblurring-using-spatially","title":"Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/iterative-residual-image-deconvolution","slug":"iterative-residual-image-deconvolution","title":"Iterative Residual Image Deconvolution","date":"2018-04-17","arxiv_id":"1804.06042","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-extract-a-video-sequence-from-a","slug":"learning-to-extract-a-video-sequence-from-a","title":"Learning to Extract a Video Sequence from a Single Motion-Blurred Image","date":"2018-04-11","arxiv_id":"1804.04065","repositories_listed":1,"syntology":null},{"url":"/paper/learning-an-optimizer-for-image-deconvolution","slug":"learning-an-optimizer-for-image-deconvolution","title":"Learning Deep Gradient Descent Optimization for Image Deconvolution","date":"2018-04-10","arxiv_id":"1804.03368","repositories_listed":1,"syntology":null},{"url":"/paper/bringing-alive-blurred-moments","slug":"bringing-alive-blurred-moments","title":"Bringing Alive Blurred Moments","date":"2018-04-09","arxiv_id":"1804.02913","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-spatio-temporal-learning-for","slug":"adversarial-spatio-temporal-learning-for","title":"Adversarial Spatio-Temporal Learning for Video Deblurring","date":"2018-03-28","arxiv_id":"1804.00533","repositories_listed":1,"syntology":null},{"url":"/paper/blind-image-deconvolution-using-deep","slug":"blind-image-deconvolution-using-deep","title":"Blind Image Deconvolution using Deep Generative Priors","date":"2018-02-12","arxiv_id":"1802.04073","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/blind-image-deconvolution-using-deep#ran","syntology_url":"https://syntology.ai/paper/1802.04073","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.04073"}},"official":null}},{"url":"/paper/denoising-prior-driven-deep-neural-network","slug":"denoising-prior-driven-deep-neural-network","title":"Denoising Prior Driven Deep Neural Network for Image Restoration","date":"2018-01-21","arxiv_id":"1801.06756","repositories_listed":1,"syntology":null},{"url":"/paper/deep-mean-shift-priors-for-image-restoration","slug":"deep-mean-shift-priors-for-image-restoration","title":"Deep Mean-Shift Priors for Image Restoration","date":"2017-09-12","arxiv_id":"1709.03749","repositories_listed":1,"syntology":null},{"url":"/paper/deep-generative-filter-for-motion-deblurring","slug":"deep-generative-filter-for-motion-deblurring","title":"Deep Generative Filter for Motion Deblurring","date":"2017-09-11","arxiv_id":"1709.03481","repositories_listed":1,"syntology":null},{"url":"/paper/learning-blind-motion-deblurring","slug":"learning-blind-motion-deblurring","title":"Learning Blind Motion Deblurring","date":"2017-08-14","arxiv_id":"1708.04208","repositories_listed":1,"syntology":null},{"url":"/paper/deep-video-deblurring-for-hand-held-cameras","slug":"deep-video-deblurring-for-hand-held-cameras","title":"Deep Video Deblurring for Hand-Held Cameras","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/understanding-kernel-size-in-blind","slug":"understanding-kernel-size-in-blind","title":"Understanding Kernel Size in Blind Deconvolution","date":"2017-06-06","arxiv_id":"1706.01797","repositories_listed":1,"syntology":null},{"url":"/paper/dirty-pixels-optimizing-image-classification","slug":"dirty-pixels-optimizing-image-classification","title":"Dirty Pixels: Towards End-to-End Image Processing and Perception","date":"2017-01-23","arxiv_id":"1701.06487","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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) · 4 unverified","sample_list":"/paper/dirty-pixels-optimizing-image-classification#ran","syntology_url":"https://syntology.ai/paper/1701.06487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.06487"}},"official":{"repos":["princeton-computational-imaging/DirtyPixels"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-multi-scale-convolutional-neural-network","slug":"deep-multi-scale-convolutional-neural-network","title":"Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring","date":"2016-12-07","arxiv_id":"1612.02177","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/deep-multi-scale-convolutional-neural-network#ran","syntology_url":"https://syntology.ai/paper/1612.02177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.02177"}},"official":{"repos":["SeungjunNah/DeepDeblur-PyTorch"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/on-demand-learning-for-deep-image-restoration","slug":"on-demand-learning-for-deep-image-restoration","title":"On-Demand Learning for Deep Image Restoration","date":"2016-12-05","arxiv_id":"1612.01380","repositories_listed":1,"syntology":null},{"url":"/paper/deep-video-deblurring","slug":"deep-video-deblurring","title":"Deep Video Deblurring","date":"2016-11-25","arxiv_id":"1611.08387","repositories_listed":1,"syntology":null},{"url":"/paper/patch-ordering-as-a-regularization-for","slug":"patch-ordering-as-a-regularization-for","title":"Patch-Ordering as a Regularization for Inverse Problems in Image Processing","date":"2016-02-26","arxiv_id":"1602.08510","repositories_listed":1,"syntology":null},{"url":"/paper/class-specific-image-deblurring-1","slug":"class-specific-image-deblurring-1","title":"Class-specific image deblurring","date":"2015-12-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/group-based-sparse-representation-for-image","slug":"group-based-sparse-representation-for-image","title":"Group-based Sparse Representation for Image Restoration","date":"2014-05-14","arxiv_id":"1405.3351","repositories_listed":1,"syntology":null},{"url":null,"slug":"dynamic-bandwidth-allocation-for-hybrid-event","title":"Dynamic Bandwidth Allocation for Hybrid Event-RGB Transmission","date":"2025-06-25","arxiv_id":"2506.20222","repositories_listed":0,"syntology":null},{"url":null,"slug":"restoring-gaussian-blurred-face-images-for","title":"Restoring Gaussian Blurred Face Images for Deanonymization Attacks","date":"2025-06-14","arxiv_id":"2506.12344","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-step-guided-diffusion-for-image","title":"Multi-Step Guided Diffusion for Image Restoration on Edge Devices: Toward Lightweight Perception in Embodied AI","date":"2025-06-08","arxiv_id":"2506.07286","repositories_listed":0,"syntology":null},{"url":null,"slug":"ev-layersegnet-self-supervised-motion","title":"EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras","date":"2025-06-07","arxiv_id":"2506.06596","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-inverse-problems-parameter-estimation-and","title":"On Inverse Problems, Parameter Estimation, and Domain Generalization","date":"2025-06-06","arxiv_id":"2506.06024","repositories_listed":0,"syntology":null},{"url":null,"slug":"plug-and-play-posterior-sampling-for-blind","title":"Plug-and-Play Posterior Sampling for Blind Inverse Problems","date":"2025-05-28","arxiv_id":"2505.22923","repositories_listed":0,"syntology":null},{"url":null,"slug":"spikegen-generative-framework-for-visual","title":"SpikeGen: Generative Framework for Visual Spike Stream Processing","date":"2025-05-23","arxiv_id":"2505.18049","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-flow-and-feature-refinement-using","title":"Joint Flow And Feature Refinement Using Attention For Video Restoration","date":"2025-05-22","arxiv_id":"2505.16434","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-incremental-algorithm-for-non-convex-ai","title":"An incremental algorithm for non-convex AI-enhanced medical image processing","date":"2025-05-13","arxiv_id":"2505.08324","repositories_listed":0,"syntology":null},{"url":null,"slug":"mobgs-motion-deblurring-dynamic-3d-gaussian","title":"MoBGS: Motion Deblurring Dynamic 3D Gaussian Splatting for Blurry Monocular Video","date":"2025-04-21","arxiv_id":"2504.15122","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-domain-learning-with-kernel-prior","title":"Frequency-domain Learning with Kernel Prior for Blind Image Deblurring","date":"2025-04-20","arxiv_id":"2504.14664","repositories_listed":0,"syntology":null},{"url":null,"slug":"coding-prior-guided-diffusion-network-for","title":"Coding-Prior Guided Diffusion Network for Video Deblurring","date":"2025-04-16","arxiv_id":"2504.12222","repositories_listed":0,"syntology":null},{"url":null,"slug":"ebad-gaussian-event-driven-bundle-adjusted","title":"EBAD-Gaussian: Event-driven Bundle Adjusted Deblur Gaussian Splatting","date":"2025-04-14","arxiv_id":"2504.10012","repositories_listed":0,"syntology":null},{"url":null,"slug":"astroclearnet-deep-image-prior-for-multi","title":"AstroClearNet: Deep image prior for multi-frame astronomical image restoration","date":"2025-04-08","arxiv_id":"2504.06463","repositories_listed":0,"syntology":null},{"url":null,"slug":"diet-gs-diffusion-prior-and-event-stream","title":"DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D Gaussian Splatting","date":"2025-03-31","arxiv_id":"2503.24210","repositories_listed":0,"syntology":null},{"url":null,"slug":"reld-regularization-by-latent-diffusion","title":"RELD: Regularization by Latent Diffusion Models for Image Restoration","date":"2025-03-28","arxiv_id":"2503.22563","repositories_listed":0,"syntology":null},{"url":null,"slug":"fractal-ir-a-unified-framework-for-efficient","title":"Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration","date":"2025-03-22","arxiv_id":"2503.17825","repositories_listed":0,"syntology":null},{"url":null,"slug":"bard-gs-blur-aware-reconstruction-of-dynamic","title":"BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting","date":"2025-03-20","arxiv_id":"2503.15835","repositories_listed":0,"syntology":null},{"url":null,"slug":"sir-diff-sparse-image-sets-restoration-with","title":"SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model","date":"2025-03-18","arxiv_id":"2503.14463","repositories_listed":0,"syntology":null},{"url":null,"slug":"deblur-gaussian-splatting-slam","title":"Deblur Gaussian Splatting SLAM","date":"2025-03-16","arxiv_id":"2503.12572","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruct-anything-model-a-lightweight","title":"Reconstruct Anything Model: a lightweight foundation model for computational imaging","date":"2025-03-11","arxiv_id":"2503.08915","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-infrared-image-deblurring-and-event","title":"Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging","date":"2025-03-03","arxiv_id":"2503.01193","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-differential-handling-of-various-blur","title":"Towards Differential Handling of Various Blur Regions for Accurate Image Deblurring","date":"2025-02-27","arxiv_id":"2502.19677","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-conformal-prediction-for-1","title":"Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems","date":"2025-02-26","arxiv_id":"2502.19194","repositories_listed":0,"syntology":null},{"url":null,"slug":"adjust-your-focus-defocus-deblurring-from","title":"Adjust Your Focus: Defocus Deblurring From Dual-Pixel Images Using Explicit Multi-Scale Cross-Correlation","date":"2025-02-16","arxiv_id":"2502.11002","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improved-optimal-proximal-gradient","title":"An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image Deblurring","date":"2025-02-11","arxiv_id":"2502.07602","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-conformal-prediction-for","title":"Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems","date":"2025-02-07","arxiv_id":"2502.05127","repositories_listed":0,"syntology":null},{"url":null,"slug":"unpaired-deblurring-via-decoupled-diffusion","title":"Unpaired Deblurring via Decoupled Diffusion Model","date":"2025-02-03","arxiv_id":"2502.01522","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-frame-blind-manifold-deconvolution-for","title":"Multi-Frame Blind Manifold Deconvolution for Rotating Synthetic Aperture Imaging","date":"2025-01-31","arxiv_id":"2501.19386","repositories_listed":0,"syntology":null},{"url":null,"slug":"clearsight-human-vision-inspired-solutions","title":"ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring","date":"2025-01-27","arxiv_id":"2501.15808","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-motion-blur-removal-in-the-temporal","title":"Image Motion Blur Removal in the Temporal Dimension with Video Diffusion Models","date":"2025-01-22","arxiv_id":"2501.12604","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffstereo-high-frequency-aware-diffusion","title":"DiffStereo: High-Frequency Aware Diffusion Model for Stereo Image Restoration","date":"2025-01-17","arxiv_id":"2501.10325","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-transmission-and-deblurring-a-semantic","title":"Joint Transmission and Deblurring: A Semantic Communication Approach Using Events","date":"2025-01-16","arxiv_id":"2501.09396","repositories_listed":0,"syntology":null},{"url":null,"slug":"soft-knowledge-distillation-with-multi","title":"Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression","date":"2025-01-16","arxiv_id":"2501.09321","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-test-time-adaptation-for-single","title":"Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks","date":"2025-01-15","arxiv_id":"2501.09052","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-mutual-information-based-1","title":"Conditional Mutual Information Based Diffusion Posterior Sampling for Solving Inverse Problems","date":"2025-01-06","arxiv_id":"2501.02880","repositories_listed":0,"syntology":null},{"url":"/paper/a-polarization-aided-transformer-for-image","slug":"a-polarization-aided-transformer-for-image","title":"A Polarization-Aided Transformer for Image Deblurring via Motion Vector Decomposition","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamode-nerf-motion-aware-deblurring-neural","title":"DynaMoDe-NeRF: Motion-aware Deblurring Neural Radiance Field for Dynamic Scenes","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parameterized-blur-kernel-prior-learning-for","title":"Parameterized Blur Kernel Prior Learning for Local Motion Deblurring","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quad-pixel-image-defocus-deblurring-a-new","title":"Quad-Pixel Image Defocus Deblurring: A New Benchmark and Model","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-diffusion-models-for-inverse","title":"Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling","date":"2024-12-28","arxiv_id":"2412.20045","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-network-on-motion-blur","title":"Generative Adversarial Network on Motion-Blur Image Restoration","date":"2024-12-27","arxiv_id":"2412.19479","repositories_listed":0,"syntology":null},{"url":null,"slug":"event-based-motion-deblurring-via-multi","title":"Event-based Motion Deblurring via Multi-Temporal Granularity Fusion","date":"2024-12-16","arxiv_id":"2412.11866","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-context-aware-convolutional-network","title":"Towards Context-aware Convolutional Network for Image Restoration","date":"2024-12-15","arxiv_id":"2412.11008","repositories_listed":0,"syntology":null},{"url":"/paper/xyscannet-an-interpretable-state-space-model","slug":"xyscannet-an-interpretable-state-space-model","title":"XYScanNet: A State Space Model for Single Image Deblurring","date":"2024-12-13","arxiv_id":"2412.10338","repositories_listed":0,"syntology":null},{"url":null,"slug":"exprdiff-short-exposure-guided-diffusion","title":"ExpRDiff: Short-exposure Guided Diffusion Model for Realistic Local Motion Deblurring","date":"2024-12-12","arxiv_id":"2412.09193","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-meets-pixels-pde-models-in-image","title":"Physics Meets Pixels: PDE Models in Image Processing","date":"2024-12-11","arxiv_id":"2412.11946","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-progressive-image-restoration-network-for","title":"A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing","date":"2024-12-10","arxiv_id":"2412.07195","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-joint-unrolling-for-deblurring-and-low","title":"Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement (JUDE)","date":"2024-12-10","arxiv_id":"2412.07527","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-lidar-guided-image-deblurring","title":"Deep Lidar-guided Image Deblurring","date":"2024-12-10","arxiv_id":"2412.07262","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-motion-blur-and-deblurring-in-visual-place","title":"On Motion Blur and Deblurring in Visual Place Recognition","date":"2024-12-10","arxiv_id":"2412.07751","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-sample-generation-of-diffusion","title":"Enhancing Sample Generation of Diffusion Models using Noise Level Correction","date":"2024-12-07","arxiv_id":"2412.05488","repositories_listed":0,"syntology":null},{"url":null,"slug":"dawn-si-data-aware-and-noise-informed","title":"DAWN-FM: Data-Aware and Noise-Informed Flow Matching for Solving Inverse Problems","date":"2024-12-06","arxiv_id":"2412.04766","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-priors-for-satellite-image-restoration","title":"Deep priors for satellite image restoration with accurate uncertainties","date":"2024-12-05","arxiv_id":"2412.04130","repositories_listed":0,"syntology":null},{"url":null,"slug":"divd-deblurring-with-improved-video-diffusion","title":"DIVD: Deblurring with Improved Video Diffusion Model","date":"2024-12-01","arxiv_id":"2412.00773","repositories_listed":0,"syntology":null},{"url":null,"slug":"unleashing-the-power-of-data-synthesis-in","title":"Unleashing the Power of Data Synthesis in Visual Localization","date":"2024-11-28","arxiv_id":"2412.00138","repositories_listed":0,"syntology":null},{"url":null,"slug":"vipaint-image-inpainting-with-pre-trained","title":"VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference","date":"2024-11-28","arxiv_id":"2411.18929","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-lensless-image-deblurring-with-prior","title":"Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime","date":"2024-11-27","arxiv_id":"2411.18189","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-guided-posterior-sampling-for","title":"Frequency-Guided Posterior Sampling for Diffusion-Based Image Restoration","date":"2024-11-22","arxiv_id":"2411.15295","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-aware-guidance-for-blind-image","title":"Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models","date":"2024-11-19","arxiv_id":"2411.12450","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-diffusion-posterior-sampling-for","title":"Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements","date":"2024-11-15","arxiv_id":"2411.09850","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-adapting-randomized-nystrom","title":"On Adapting Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction","date":"2024-11-12","arxiv_id":"2411.08178","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-frequency-enhancement-network-for","title":"Multi-scale Frequency Enhancement Network for Blind Image Deblurring","date":"2024-11-11","arxiv_id":"2411.06893","repositories_listed":0,"syntology":null},{"url":null,"slug":"dropout-the-high-rate-downsampling-a-novel","title":"Dropout the High-rate Downsampling: A Novel Design Paradigm for UHD Image Restoration","date":"2024-11-10","arxiv_id":"2411.06456","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-modular-conditional-diffusion-framework-for","title":"A Modular Conditional Diffusion Framework for Image Reconstruction","date":"2024-11-08","arxiv_id":"2411.05993","repositories_listed":0,"syntology":null},{"url":null,"slug":"constrained-diffusion-implicit-models","title":"Constrained Diffusion Implicit Models","date":"2024-11-01","arxiv_id":"2411.00359","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-diffusion-model-from-noisy","title":"Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method","date":"2024-10-15","arxiv_id":"2410.11241","repositories_listed":0,"syntology":null},{"url":null,"slug":"glmha-a-guided-low-rank-multi-head-self","title":"GLMHA A Guided Low-rank Multi-Head Self-Attention for Efficient Image Restoration and Spectral Reconstruction","date":"2024-10-01","arxiv_id":"2410.00380","repositories_listed":0,"syntology":null},{"url":null,"slug":"dap-led-learning-degradation-aware-priors","title":"DAP-LED: Learning Degradation-Aware Priors with CLIP for Joint Low-light Enhancement and Deblurring","date":"2024-09-20","arxiv_id":"2409.13496","repositories_listed":0,"syntology":null},{"url":null,"slug":"taming-diffusion-models-for-image-restoration","title":"Taming Diffusion Models for Image Restoration: A Review","date":"2024-09-16","arxiv_id":"2409.10353","repositories_listed":0,"syntology":null},{"url":null,"slug":"think-twice-before-you-act-improving-inverse","title":"Think Twice Before You Act: Improving Inverse Problem Solving With MCMC","date":"2024-09-13","arxiv_id":"2409.08551","repositories_listed":0,"syntology":null},{"url":null,"slug":"quaternion-nuclear-norm-minus-frobenius-norm","title":"Quaternion Nuclear Norm minus Frobenius Norm Minimization for color image reconstruction","date":"2024-09-12","arxiv_id":"2409.07797","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-bayesian-image-restoration-by","title":"Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior","date":"2024-09-06","arxiv_id":"2409.04384","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-video-inverse-problems-using-image","title":"Solving Video Inverse Problems Using Image Diffusion Models","date":"2024-09-04","arxiv_id":"2409.02574","repositories_listed":0,"syntology":null},{"url":null,"slug":"f2former-when-fractional-fourier-meets-deep","title":"F2former: When Fractional Fourier Meets Deep Wiener Deconvolution and Selective Frequency Transformer for Image Deblurring","date":"2024-09-03","arxiv_id":"2409.02056","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-multi-scale-network-for-blind","title":"Self-Supervised Multi-Scale Network for Blind Image Deblurring via Alternating Optimization","date":"2024-09-02","arxiv_id":"2409.00988","repositories_listed":0,"syntology":null}],"record_sha256":"499d98f71e808d280cd484bce22961ae2d91a05707a106a969a42793e7d1785a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}