{"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/calc-ssim","entry":"calc_ssim","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":6,"n_papers_ran":2,"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":6,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":4},"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":"2407.09059","paper":"/paper/domain-adaptive-video-deblurring-via-test","title":"Domain-adaptive Video Deblurring via Test-time Blurring","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jin-ting-he/dadeblur","path":"DeblurringModel/ESTRNN/deblur_inference.py","file_url":"https://github.com/jin-ting-he/dadeblur/blob/HEAD/DeblurringModel/ESTRNN/deblur_inference.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9072f9b136f81f59","mcp_get_code":{"code_sha256":"9072f9b136f81f59"}},{"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/RMSE.py","file_url":"https://github.com/YhuoyuH/RetinexMamba/blob/HEAD/Enhancement/RMSE.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eb63f9501a5d6798","mcp_get_code":{"code_sha256":"eb63f9501a5d6798"}},{"arxiv_id":"2303.16206","paper":"/paper/learning-iterative-neural-optimizers-for","title":"Learning Iterative Neural Optimizers for Image Steganography","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cxy1997/LISO","path":"liso/models.py","file_url":"https://github.com/cxy1997/LISO/blob/HEAD/liso/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4f295d47ee8d9f62","mcp_get_code":{"code_sha256":"4f295d47ee8d9f62"}},{"arxiv_id":"2104.00416","paper":"/paper/unsupervised-degradation-representation","title":"Unsupervised Degradation Representation Learning for Blind Super-Resolution","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LongguangWang/DASR","path":"utility.py","file_url":"https://github.com/LongguangWang/DASR/blob/HEAD/utility.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"351137eb5d75b2c1","mcp_get_code":{"code_sha256":"351137eb5d75b2c1"}},{"arxiv_id":"2006.04139","paper":"/paper/learning-texture-transformer-network-for-1","title":"Learning Texture Transformer Network for Image Super-Resolution","date":"2020-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"researchmm/TTSR","path":"utils.py","file_url":"https://github.com/researchmm/TTSR/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":"47818e30712d8b00","mcp_get_code":{"code_sha256":"47818e30712d8b00"}},{"arxiv_id":"2004.03791","paper":"/paper/learning-for-scale-arbitrary-super-resolution","title":"Learning A Single Network for Scale-Arbitrary Super-Resolution","date":"2020-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LongguangWang/ArbSR","path":"utility.py","file_url":"https://github.com/LongguangWang/ArbSR/blob/HEAD/utility.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1a21a8c2563f4921","mcp_get_code":{"code_sha256":"1a21a8c2563f4921"}}]}