{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/code/calculate-psnr","entry":"calculate_psnr","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":51,"n_papers_ran":43,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":28,"n_samples_ran":20,"n_samples_fingerprinted":9,"n_places":52,"n_places_pointer_only":21,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":2,"ran":17,"unverified":8},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2608.23799","paper":"/paper/arxiv-2608-23799","title":"Restoring Without Forgetting: Continual Learning Across Image Degradations","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"AlifAshrafee/Restoring-Without-Forgetting","path":"restormer/Motion_Deblurring/utils.py","file_url":"https://github.com/AlifAshrafee/Restoring-Without-Forgetting/blob/HEAD/restormer/Motion_Deblurring/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d292dd965ab59629","mcp_get_code":{"code_sha256":"d292dd965ab59629"}},{"arxiv_id":"2607.17849","paper":"/paper/arxiv-2607-17849","title":"AlphaOracle: Oracle bone script decipherment via human-workflow-inspired deep learning","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"Yuliang-Liu/AlphaOracle","path":"models/Rubbing_parsing/ConversionDiT/evaluate_tokenizer.py","file_url":"https://github.com/Yuliang-Liu/AlphaOracle/blob/HEAD/models/Rubbing_parsing/ConversionDiT/evaluate_tokenizer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7890f73a78eea59b","mcp_get_code":{"code_sha256":"7890f73a78eea59b"}},{"arxiv_id":"2606.30821","paper":"/paper/arxiv-2606-30821","title":"Mind the Residual Gap: Probabilistic Downscaling under Real-World Bias","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"JingyunLiang/SwinIR","path":"utils/util_calculate_psnr_ssim.py","file_url":"https://github.com/JingyunLiang/SwinIR/blob/HEAD/utils/util_calculate_psnr_ssim.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"eaaa41d8e8354993","mcp_get_code":{"code_sha256":"eaaa41d8e8354993"}},{"arxiv_id":"2605.00310","paper":"/paper/arxiv-2605-00310","title":"Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"ai-spatial/GeoSR-Bench","path":"MODIS_L8/SR_Models/CFAT_M2L8/util_calculate_psnr_ssim.py","file_url":"https://github.com/ai-spatial/GeoSR-Bench/blob/HEAD/MODIS_L8/SR_Models/CFAT_M2L8/util_calculate_psnr_ssim.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2a71b83d717fc9be","mcp_get_code":{"code_sha256":"2a71b83d717fc9be"}},{"arxiv_id":"2508.02168","paper":"/paper/arxiv-2508-02168","title":"After the Party: Navigating the Mapping From Color to Ambient Lighting","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"fvasluianu97/RLN2","path":"utils.py","file_url":"https://github.com/fvasluianu97/RLN2/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d292dd965ab59629","mcp_get_code":{"code_sha256":"d292dd965ab59629"}},{"arxiv_id":"2506.00329","paper":null,"title":"arXiv:2506.00329","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"STAR-Laboratory/foresight","path":"eval/foresight/common_metrics/calculate_psnr.py","file_url":"https://github.com/STAR-Laboratory/foresight/blob/HEAD/eval/foresight/common_metrics/calculate_psnr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ed63913e22ffef2b","mcp_get_code":{"code_sha256":"ed63913e22ffef2b"}},{"arxiv_id":"2505.23068","paper":"/paper/urwkv-unified-rwkv-model-with-multi-state","title":"URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration","date":"2025-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FZU-N/URWKV","path":"custom_utils/image_utils.py","file_url":"https://github.com/FZU-N/URWKV/blob/HEAD/custom_utils/image_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d292dd965ab59629","mcp_get_code":{"code_sha256":"d292dd965ab59629"}},{"arxiv_id":"2411.15262","paper":"/paper/moviebench-a-hierarchical-movie-level-dataset","title":"MovieBench: A Hierarchical Movie Level Dataset for Long Video Generation","date":"2024-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"showlab/moviebecnh","path":"metrics/Metric_6_FVD/calculate_psnr.py","file_url":"https://github.com/showlab/moviebecnh/blob/HEAD/metrics/Metric_6_FVD/calculate_psnr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"270b7c941a61baa4","mcp_get_code":{"code_sha256":"270b7c941a61baa4"}},{"arxiv_id":"2410.23530","paper":"/paper/there-and-back-again-on-the-relation-between","title":"There and Back Again: On the relation between noises, images, and their inversions in diffusion models","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luk-st/taba","path":"taba/metrics/alignment.py","file_url":"https://github.com/luk-st/taba/blob/HEAD/taba/metrics/alignment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d5191804ff8fa769","mcp_get_code":{"code_sha256":"d5191804ff8fa769"}},{"arxiv_id":"2410.21535","paper":"/paper/ecmamba-consolidating-selective-state-space","title":"ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction","date":"2024-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LowlevelAI/ECMamba","path":"utils.py","file_url":"https://github.com/LowlevelAI/ECMamba/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d292dd965ab59629","mcp_get_code":{"code_sha256":"d292dd965ab59629"}},{"arxiv_id":"2410.08151","paper":"/paper/progressive-autoregressive-video-diffusion","title":"Progressive Autoregressive Video Diffusion Models","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"desaixie/pa_vdm","path":"eval/vae/cal_psnr.py","file_url":"https://github.com/desaixie/pa_vdm/blob/HEAD/eval/vae/cal_psnr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1d84e2bdaf34e46f","mcp_get_code":{"code_sha256":"1d84e2bdaf34e46f"}},{"arxiv_id":"2409.01641","paper":"/paper/unveiling-advanced-frequency-disentanglement","title":"Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement","date":"2024-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"redrock303/ADF-LLIE","path":"LoLv2/util.py","file_url":"https://github.com/redrock303/ADF-LLIE/blob/HEAD/LoLv2/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"eaaa41d8e8354993","mcp_get_code":{"code_sha256":"eaaa41d8e8354993"}},{"arxiv_id":"2409.01641","paper":"/paper/unveiling-advanced-frequency-disentanglement","title":"Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement","date":"2024-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"redrock303/ADF-LLIE","path":"utils/common.py","file_url":"https://github.com/redrock303/ADF-LLIE/blob/HEAD/utils/common.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"2407.16125","paper":"/paper/diffusion-prior-based-amortized-variational","title":"Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kdhRick2222/Exposure-slot","path":"utils/util.py","file_url":"https://github.com/kdhRick2222/Exposure-slot/blob/HEAD/utils/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"515cde57deed08fc","mcp_get_code":{"code_sha256":"515cde57deed08fc"}},{"arxiv_id":"2407.09299","paper":"/paper/pid-physics-informed-diffusion-model-for","title":"PID: Physics-Informed Diffusion Model for Infrared Image Generation","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fangyuanmao/pid","path":"metric/core/metrics.py","file_url":"https://github.com/fangyuanmao/pid/blob/HEAD/metric/core/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd6ad9d6b59b892f","mcp_get_code":{"code_sha256":"dd6ad9d6b59b892f"}},{"arxiv_id":"2407.05680","paper":"/paper/fine-grained-multi-view-hand-reconstruction","title":"Fine-Grained Multi-View Hand Reconstruction Using Inverse Rendering","date":"2024-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"agnjason/fmhr","path":"train_unet.py","file_url":"https://github.com/agnjason/fmhr/blob/HEAD/train_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e7c54f2a25d9ee14","mcp_get_code":{"code_sha256":"e7c54f2a25d9ee14"}},{"arxiv_id":"2407.00788","paper":"/paper/instantstyle-plus-style-transfer-with-content","title":"InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation","date":"2024-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"instantx-research/instantstyle-plus","path":"src/metrics/metric_util.py","file_url":"https://github.com/instantx-research/instantstyle-plus/blob/HEAD/src/metrics/metric_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"62635133d9c50017","mcp_get_code":{"code_sha256":"62635133d9c50017"}},{"arxiv_id":"2406.00508","paper":"/paper/flowie-efficient-image-enhancement-via","title":"FlowIE: Efficient Image Enhancement via Rectified Flow","date":"2024-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EternalEvan/FlowIE","path":"evaluate.py","file_url":"https://github.com/EternalEvan/FlowIE/blob/HEAD/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"503dc92a5460ffce","mcp_get_code":{"code_sha256":"503dc92a5460ffce"}},{"arxiv_id":"2405.17074","paper":"/paper/towards-ultra-high-definition-image-deraining","title":"Towards Ultra-High-Definition Image Deraining: A Benchmark and An Efficient Method","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cschenxiang/udr-mixer","path":"metrics/PSNR_SSIM.py","file_url":"https://github.com/cschenxiang/udr-mixer/blob/HEAD/metrics/PSNR_SSIM.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"2405.06277","paper":"/paper/learning-a-spiking-neural-network-for","title":"Learning A Spiking Neural Network for Efficient Image Deraining","date":"2024-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MingTian99/ESDNet","path":"evaluation.py","file_url":"https://github.com/MingTian99/ESDNet/blob/HEAD/evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"2405.03349","paper":"/paper/retinexmamba-retinex-based-mamba-for-low","title":"Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YhuoyuH/RetinexMamba","path":"Enhancement/utils.py","file_url":"https://github.com/YhuoyuH/RetinexMamba/blob/HEAD/Enhancement/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d292dd965ab59629","mcp_get_code":{"code_sha256":"d292dd965ab59629"}},{"arxiv_id":"2403.10362","paper":"/paper/cpga-coding-priors-guided-aggregation-network","title":"CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VQE-CPGA/CPGA","path":"utils/metrics.py","file_url":"https://github.com/VQE-CPGA/CPGA/blob/HEAD/utils/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"de8815c7134a7637","mcp_get_code":{"code_sha256":"de8815c7134a7637"}},{"arxiv_id":"2403.01497","paper":"/paper/learning-a-physical-aware-diffusion-model","title":"Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenydong/pa-diff","path":"core/metrics.py","file_url":"https://github.com/chenydong/pa-diff/blob/HEAD/core/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"2402.05773","paper":"/paper/uav-rain1k-a-benchmark-for-raindrop-removal","title":"UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery","date":"2024-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cschenxiang/uav-rain1k","path":"evaluation.py","file_url":"https://github.com/cschenxiang/uav-rain1k/blob/HEAD/evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"2312.00210","paper":"/paper/dream-diffusion-rectification-and-estimation","title":"DREAM: Diffusion Rectification and Estimation-Adaptive Models","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinxinzhou/dream","path":"sr3/core/metrics.py","file_url":"https://github.com/jinxinzhou/dream/blob/HEAD/sr3/core/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"2310.20332","paper":"/paper/recaptured-raw-screen-image-and-video-1","title":"Recaptured Raw Screen Image and Video Demoiréing via Channel and Spatial Modulations","date":"2023-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tju-chengyijia/vd_raw","path":"cal_psnr_ssim_lpips.py","file_url":"https://github.com/tju-chengyijia/vd_raw/blob/HEAD/cal_psnr_ssim_lpips.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c7a8165906af2d57","mcp_get_code":{"code_sha256":"c7a8165906af2d57"}},{"arxiv_id":"2309.13890","paper":"/paper/bitstream-corrupted-video-recovery-a-novel-1","title":"Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method","date":"2023-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LIUTIGHE/BSCV-Dataset","path":"core/metrics.py","file_url":"https://github.com/LIUTIGHE/BSCV-Dataset/blob/HEAD/core/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"eb1acaefb4b711f8","mcp_get_code":{"code_sha256":"eb1acaefb4b711f8"}},{"arxiv_id":"2309.13039","paper":"/paper/nerrf-3d-reconstruction-and-view-synthesis","title":"NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dawning77/nerrf","path":"eval/eval_approx.py","file_url":"https://github.com/dawning77/nerrf/blob/HEAD/eval/eval_approx.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4bca9dffc88981f7","mcp_get_code":{"code_sha256":"4bca9dffc88981f7"}},{"arxiv_id":"2309.03063","paper":"/paper/prompt-based-all-in-one-image-restoration","title":"Prompt-based Ingredient-Oriented All-in-One Image Restoration","date":"2023-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tombs98/CAPTNet","path":"cal.py","file_url":"https://github.com/Tombs98/CAPTNet/blob/HEAD/cal.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b0a14f63a15e798e","mcp_get_code":{"code_sha256":"b0a14f63a15e798e"}},{"arxiv_id":"2308.14409","paper":"/paper/steerable-conditional-diffusion-for-out-of","title":"Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction","date":"2023-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexdenker/SteerableConditionalDiffusion","path":"conditional_sampling.py","file_url":"https://github.com/alexdenker/SteerableConditionalDiffusion/blob/HEAD/conditional_sampling.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6ec72d1ab0964abc","mcp_get_code":{"code_sha256":"6ec72d1ab0964abc"}},{"arxiv_id":"2308.14036","paper":"/paper/mb-taylorformer-multi-branch-efficient","title":"MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing","date":"2023-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fvl2020/iccv-2023-mb-taylorformer","path":"utils.py","file_url":"https://github.com/fvl2020/iccv-2023-mb-taylorformer/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d292dd965ab59629","mcp_get_code":{"code_sha256":"d292dd965ab59629"}},{"arxiv_id":"2308.03867","paper":"/paper/from-sky-to-the-ground-a-large-scale","title":"From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal","date":"2023-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunguo224/lhp-rain","path":"SCD-Former/utils/caculate_psnr_ssim.py","file_url":"https://github.com/yunguo224/lhp-rain/blob/HEAD/SCD-Former/utils/caculate_psnr_ssim.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ca23a11161d101d9","mcp_get_code":{"code_sha256":"ca23a11161d101d9"}},{"arxiv_id":"2308.01738","paper":"/paper/enhancing-visibility-in-nighttime-haze-images","title":"Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution","date":"2023-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinyeying/nighttime_dehaze","path":"0_ACMMM23_RESULTS/calculate_psnr_ssim_NH_GTA5.py","file_url":"https://github.com/jinyeying/nighttime_dehaze/blob/HEAD/0_ACMMM23_RESULTS/calculate_psnr_ssim_NH_GTA5.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7f3a54e16f28ef7e","mcp_get_code":{"code_sha256":"7f3a54e16f28ef7e"}},{"arxiv_id":"2304.01994","paper":"/paper/dwa-differential-wavelet-amplifier-for-image","title":"Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution","date":"2023-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brian-moser/diwa","path":"core/metrics.py","file_url":"https://github.com/brian-moser/diwa/blob/HEAD/core/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"2303.11684","paper":"/paper/spikecv-open-a-continuous-computer-vision-era","title":"SpikeCV: Open a Continuous Computer Vision Era","date":"2023-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyj061/spikecv","path":"SpikeCV/metrics/reconsturction.py","file_url":"https://github.com/zyj061/spikecv/blob/HEAD/SpikeCV/metrics/reconsturction.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2b4a6b7700cb4268","mcp_get_code":{"code_sha256":"2b4a6b7700cb4268"}},{"arxiv_id":"2303.02881","paper":"/paper/kbnet-kernel-basis-network-for-image","title":"KBNet: Kernel Basis Network for Image Restoration","date":"2023-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangyi-3/kbnet","path":"Denoising/utils_tool.py","file_url":"https://github.com/zhangyi-3/kbnet/blob/HEAD/Denoising/utils_tool.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d292dd965ab59629","mcp_get_code":{"code_sha256":"d292dd965ab59629"}},{"arxiv_id":"2211.11082","paper":"/paper/dynibar-neural-dynamic-image-based-rendering","title":"DynIBaR: Neural Dynamic Image-Based Rendering","date":"2022-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google/dynibar","path":"eval_nvidia.py","file_url":"https://github.com/google/dynibar/blob/HEAD/eval_nvidia.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"30e86b1a7e0e31b6","mcp_get_code":{"code_sha256":"30e86b1a7e0e31b6"}},{"arxiv_id":"2208.11284","paper":"/paper/at-ddpm-restoring-faces-degraded-by","title":"AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models","date":"2022-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nithin-gk/at-ddpm","path":"core/metrics.py","file_url":"https://github.com/nithin-gk/at-ddpm/blob/HEAD/core/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"2207.14626","paper":"/paper/restoring-vision-in-adverse-weather","title":"Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models","date":"2022-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"igitugraz/weatherdiffusion","path":"utils/metrics.py","file_url":"https://github.com/igitugraz/weatherdiffusion/blob/HEAD/utils/metrics.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f3a54e16f28ef7e","mcp_get_code":{"code_sha256":"7f3a54e16f28ef7e"}},{"arxiv_id":"2206.11892","paper":"/paper/remote-sensing-change-detection-segmentation","title":"DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection","date":"2022-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wgcban/ddpm-cd","path":"core/metrics.py","file_url":"https://github.com/wgcban/ddpm-cd/blob/HEAD/core/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"2201.02973","paper":"/paper/maxim-multi-axis-mlp-for-image-processing","title":"MAXIM: Multi-Axis MLP for Image Processing","date":"2022-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vztu/maxim-pytorch","path":"Deblurring/utils.py","file_url":"https://github.com/vztu/maxim-pytorch/blob/HEAD/Deblurring/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d292dd965ab59629","mcp_get_code":{"code_sha256":"d292dd965ab59629"}},{"arxiv_id":"2111.12294","paper":"/paper/an-image-patch-is-a-wave-phase-aware-vision","title":"An Image Patch is a Wave: Phase-Aware Vision MLP","date":"2021-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kingcong/models","path":"brdnet/cal_psnr.py","file_url":"https://github.com/kingcong/models/blob/HEAD/brdnet/cal_psnr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"17861669c3cd2ba9","mcp_get_code":{"code_sha256":"17861669c3cd2ba9"}},{"arxiv_id":"2005.05650","paper":"/paper/invertible-image-rescaling","title":"Invertible Image Rescaling","date":"2020-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pkuxmq/Invertible-Image-Rescaling","path":"metrics/calculate_PSNR_SSIM.py","file_url":"https://github.com/pkuxmq/Invertible-Image-Rescaling/blob/HEAD/metrics/calculate_PSNR_SSIM.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"1907.03222","paper":"/paper/irnet-a-general-purpose-deep-residual","title":"IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery","date":"2019-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyqcom/irn","path":"src/utils/util.py","file_url":"https://github.com/lyqcom/irn/blob/HEAD/src/utils/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"1905.02716","paper":"/paper/edvr-video-restoration-with-enhanced","title":"EDVR: Video Restoration with Enhanced Deformable Convolutional Networks","date":"2019-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhusiling/EDVR","path":"codes/utils/util.py","file_url":"https://github.com/zhusiling/EDVR/blob/HEAD/codes/utils/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"1904.03377","paper":"/paper/blind-super-resolution-with-iterative-kernel","title":"Blind Super-Resolution With Iterative Kernel Correction","date":"2019-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanjunchai/IKC","path":"metrics/calculate_PSNR_SSIM.py","file_url":"https://github.com/yuanjunchai/IKC/blob/HEAD/metrics/calculate_PSNR_SSIM.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"1809.00219","paper":"/paper/esrgan-enhanced-super-resolution-generative","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks","date":"2018-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sdauzcm/sr-basicsr","path":"metrics/calculate_PSNR_SSIM.py","file_url":"https://github.com/sdauzcm/sr-basicsr/blob/HEAD/metrics/calculate_PSNR_SSIM.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"1609.07009","paper":"/paper/is-the-deconvolution-layer-the-same-as-a","title":"Is the deconvolution layer the same as a convolutional layer?","date":"2016-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anujdutt9/ESPCN","path":"utils.py","file_url":"https://github.com/anujdutt9/ESPCN/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"84054f1f501dc0ba","mcp_get_code":{"code_sha256":"84054f1f501dc0ba"}},{"arxiv_id":"openreview_Y3cUZ8fNnu","paper":null,"title":"arXiv:openreview_Y3cUZ8fNnu","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"chouliuzuo/IVQ","path":"vision_experiment/score/score_functions.py","file_url":"https://github.com/chouliuzuo/IVQ/blob/HEAD/vision_experiment/score/score_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5aa3c654a73330b1","mcp_get_code":{"code_sha256":"5aa3c654a73330b1"}},{"arxiv_id":"Zamir_Restormer_Efficient_Transformer_for_High-Resolution_Image_Restoration_CVPR_2022_paper","paper":null,"title":"arXiv:Zamir_Restormer_Efficient_Transformer_for_High-Resolution_Image_Restoration_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"swz30/Restormer","path":"Denoising/utils.py","file_url":"https://github.com/swz30/Restormer/blob/HEAD/Denoising/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d292dd965ab59629","mcp_get_code":{"code_sha256":"d292dd965ab59629"}},{"arxiv_id":"Xie_Diffusion-based_Event_Generation_for_High-Quality_Image_Deblurring_CVPR_2025_paper","paper":null,"title":"arXiv:Xie_Diffusion-based_Event_Generation_for_High-Quality_Image_Deblurring_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"XinanXie/EGDeblurring","path":"core/metrics.py","file_url":"https://github.com/XinanXie/EGDeblurring/blob/HEAD/core/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9394ff2c25b8a988","mcp_get_code":{"code_sha256":"9394ff2c25b8a988"}},{"arxiv_id":"136970568","paper":null,"title":"arXiv:136970568","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cvlab-stonybrook/Iso-UVField","path":"code/evaluation/unwarp_colmap.py","file_url":"https://github.com/cvlab-stonybrook/Iso-UVField/blob/HEAD/code/evaluation/unwarp_colmap.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1c624e9a4d54bf2","mcp_get_code":{"code_sha256":"a1c624e9a4d54bf2"}}]}