{"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":"/dataset/urban100/papers/ran/1","list_of":"/dataset/urban100","dataset":"Urban100","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_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'","samples_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)"},"order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this dataset or check it against this dataset's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,26],"of":26,"counts":{"papers_with_a_benchmark_row":78,"with_a_code_link":71,"where_syntology_ran_a_sample":26,"not_listed_spam_title":0,"listed":78,"listed_where_code_ran":26,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":24,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":24,"listed_every_run_a_failure_of_syntologys_instrument":2,"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 with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/urban100/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/progressive-focused-transformer-for-single","slug":"progressive-focused-transformer-for-single","title":"Progressive Focused Transformer for Single Image Super-Resolution","date":"2025-03-26","arxiv_id":"2503.20337","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":6,"samples_unverified":1,"pointer_only_for_licence":3,"official":{"repos":["labshuhanggu/pft-sr"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/progressive-focused-transformer-for-single#ran","syntology_url":"https://syntology.ai/paper/2503.20337","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.20337"}}}},{"paper":"/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","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":6,"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"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","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"}}}},{"paper":"/paper/drct-saving-image-super-resolution-away-from","slug":"drct-saving-image-super-resolution-away-from","title":"DRCT: Saving Image Super-resolution away from Information Bottleneck","date":"2024-03-31","arxiv_id":"2404.00722","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":4,"samples_unverified":0,"pointer_only_for_licence":3,"official":{"repos":["ming053l/drct"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/drct-saving-image-super-resolution-away-from#ran","syntology_url":"https://syntology.ai/paper/2404.00722","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00722"}}}},{"paper":"/paper/kbnet-kernel-basis-network-for-image","slug":"kbnet-kernel-basis-network-for-image","title":"KBNet: Kernel Basis Network for Image Restoration","date":"2023-03-06","arxiv_id":"2303.02881","rows_on_this_dataset":6,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":5,"official":{"repos":["zhangyi-3/kbnet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/kbnet-kernel-basis-network-for-image#ran","syntology_url":"https://syntology.ai/paper/2303.02881","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.02881"}}}},{"paper":"/paper/practical-blind-denoising-via-swin-conv-unet","slug":"practical-blind-denoising-via-swin-conv-unet","title":"Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis","date":"2022-03-24","arxiv_id":"2203.13278","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":5,"official":{"repos":["cszn/scunet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/practical-blind-denoising-via-swin-conv-unet#ran","syntology_url":"https://syntology.ai/paper/2203.13278","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13278"}}}},{"paper":"/paper/restormer-efficient-transformer-for-high","slug":"restormer-efficient-transformer-for-high","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","date":"2021-11-18","arxiv_id":"2111.09881","rows_on_this_dataset":5,"code_links":13,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":4,"samples_unverified":0,"pointer_only_for_licence":4,"official":{"repos":["swz30/restormer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/restormer-efficient-transformer-for-high#ran","syntology_url":"https://syntology.ai/paper/2111.09881","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09881"}}}},{"paper":"/paper/local-texture-estimator-for-implicit","slug":"local-texture-estimator-for-implicit","title":"Local Texture Estimator for Implicit Representation Function","date":"2021-11-17","arxiv_id":"2111.08918","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":4,"samples_unverified":2,"pointer_only_for_licence":4,"official":{"repos":["jaewon-lee-b/lte"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/local-texture-estimator-for-implicit#ran","syntology_url":"https://syntology.ai/paper/2111.08918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.08918"}}}},{"paper":"/paper/swinir-image-restoration-using-swin","slug":"swinir-image-restoration-using-swin","title":"SwinIR: Image Restoration Using Swin Transformer","date":"2021-08-23","arxiv_id":"2108.10257","rows_on_this_dataset":8,"code_links":9,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":45,"samples_ran":30,"samples_constructed":14,"samples_ran_checked":16,"samples_ran_instrument_failed":14,"samples_unverified":15,"pointer_only_for_licence":5,"official":{"repos":["jingyunliang/swinir"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/swinir-image-restoration-using-swin#ran","syntology_url":"https://syntology.ai/paper/2108.10257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.10257"}}}},{"paper":"/paper/unfolding-the-alternating-optimization-for","slug":"unfolding-the-alternating-optimization-for","title":"Unfolding the Alternating Optimization for Blind Super Resolution","date":"2020-10-06","arxiv_id":"2010.02631","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":5,"samples_constructed":5,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":5,"official":{"repos":["greatlog/DAN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/unfolding-the-alternating-optimization-for#ran","syntology_url":"https://syntology.ai/paper/2010.02631","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.02631"}}}},{"paper":"/paper/lightweight-image-super-resolution-with-1","slug":"lightweight-image-super-resolution-with-1","title":"Lightweight Image Super-Resolution with Information Multi-distillation Network","date":"2019-09-26","arxiv_id":"1909.11856","rows_on_this_dataset":3,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":1,"official":{"repos":["Zheng222/IMDN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/lightweight-image-super-resolution-with-1#ran","syntology_url":"https://syntology.ai/paper/1909.11856","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.11856"}}}},{"paper":"/paper/densely-residual-laplacian-super-resolution","slug":"densely-residual-laplacian-super-resolution","title":"Densely Residual Laplacian Super-Resolution","date":"2019-06-28","arxiv_id":"1906.12021","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":1,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/densely-residual-laplacian-super-resolution#ran","syntology_url":"https://syntology.ai/paper/1906.12021","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.12021"}}}},{"paper":"/paper/blind-super-resolution-with-iterative-kernel","slug":"blind-super-resolution-with-iterative-kernel","title":"Blind Super-Resolution With Iterative Kernel Correction","date":"2019-04-06","arxiv_id":"1904.03377","rows_on_this_dataset":3,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":2,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/blind-super-resolution-with-iterative-kernel#ran","syntology_url":"https://syntology.ai/paper/1904.03377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03377"}}}},{"paper":"/paper/feedback-network-for-image-super-resolution","slug":"feedback-network-for-image-super-resolution","title":"Feedback Network for Image Super-Resolution","date":"2019-03-23","arxiv_id":"1903.09814","rows_on_this_dataset":3,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["Paper99/SRFBN_CVPR19"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/feedback-network-for-image-super-resolution#ran","syntology_url":"https://syntology.ai/paper/1903.09814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.09814"}}}},{"paper":"/paper/image-super-resolution-by-neural-texture","slug":"image-super-resolution-by-neural-texture","title":"Image Super-Resolution by Neural Texture Transfer","date":"2019-03-03","arxiv_id":"1903.00834","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":1,"official":{"repos":["ZZUTK/SRNTT"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/image-super-resolution-by-neural-texture#ran","syntology_url":"https://syntology.ai/paper/1903.00834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.00834"}}}},{"paper":"/paper/fast-accurate-and-lightweight-super","slug":"fast-accurate-and-lightweight-super","title":"Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search","date":"2019-01-22","arxiv_id":"1901.07261","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["falsr/FALSR"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/fast-accurate-and-lightweight-super#ran","syntology_url":"https://syntology.ai/paper/1901.07261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.07261"}}}},{"paper":"/paper/esrgan-enhanced-super-resolution-generative","slug":"esrgan-enhanced-super-resolution-generative","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks","date":"2018-09-01","arxiv_id":"1809.00219","rows_on_this_dataset":2,"code_links":46,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":44,"samples_ran":33,"samples_constructed":0,"samples_ran_checked":29,"samples_ran_instrument_failed":4,"samples_unverified":11,"pointer_only_for_licence":7,"official":{"repos":["xinntao/ESRGAN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/esrgan-enhanced-super-resolution-generative#ran","syntology_url":"https://syntology.ai/paper/1809.00219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.00219"}}}},{"paper":"/paper/image-super-resolution-using-very-deep","slug":"image-super-resolution-using-very-deep","title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","date":"2018-07-08","arxiv_id":"1807.02758","rows_on_this_dataset":1,"code_links":20,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":22,"samples_ran":17,"samples_constructed":6,"samples_ran_checked":16,"samples_ran_instrument_failed":1,"samples_unverified":5,"pointer_only_for_licence":3,"official":{"repos":["yulunzhang/RCAN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/image-super-resolution-using-very-deep#ran","syntology_url":"https://syntology.ai/paper/1807.02758","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.02758"}}}},{"paper":"/paper/fast-accurate-and-lightweight-super-1","slug":"fast-accurate-and-lightweight-super-1","title":"Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network","date":"2018-03-23","arxiv_id":"1803.08664","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/fast-accurate-and-lightweight-super-1#ran","syntology_url":"https://syntology.ai/paper/1803.08664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08664"}}}},{"paper":"/paper/deep-back-projection-networks-for-super","slug":"deep-back-projection-networks-for-super","title":"Deep Back-Projection Networks For Super-Resolution","date":"2018-03-07","arxiv_id":"1803.02735","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":14,"samples_ran":14,"samples_constructed":0,"samples_ran_checked":13,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":4,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/deep-back-projection-networks-for-super#ran","syntology_url":"https://syntology.ai/paper/1803.02735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.02735"}}}},{"paper":"/paper/residual-dense-network-for-image-super","slug":"residual-dense-network-for-image-super","title":"Residual Dense Network for Image Super-Resolution","date":"2018-02-24","arxiv_id":"1802.08797","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":24,"samples_ran":20,"samples_constructed":0,"samples_ran_checked":15,"samples_ran_instrument_failed":5,"samples_unverified":4,"pointer_only_for_licence":4,"official":{"repos":["yulunzhang/RDN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/residual-dense-network-for-image-super#ran","syntology_url":"https://syntology.ai/paper/1802.08797","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08797"}}}},{"paper":"/paper/ffdnet-toward-a-fast-and-flexible-solution","slug":"ffdnet-toward-a-fast-and-flexible-solution","title":"FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising","date":"2017-10-11","arxiv_id":"1710.04026","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":7,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["cszn/FFDNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/ffdnet-toward-a-fast-and-flexible-solution#ran","syntology_url":"https://syntology.ai/paper/1710.04026","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.04026"}}}},{"paper":"/paper/enhanced-deep-residual-networks-for-single","slug":"enhanced-deep-residual-networks-for-single","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","date":"2017-07-10","arxiv_id":"1707.02921","rows_on_this_dataset":1,"code_links":45,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["LimBee/NTIRE2017"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/enhanced-deep-residual-networks-for-single#ran","syntology_url":"https://syntology.ai/paper/1707.02921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.02921"}}}},{"paper":"/paper/beyond-a-gaussian-denoiser-residual-learning","slug":"beyond-a-gaussian-denoiser-residual-learning","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","date":"2016-08-13","arxiv_id":"1608.03981","rows_on_this_dataset":6,"code_links":22,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["cszn/DnCNN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/beyond-a-gaussian-denoiser-residual-learning#ran","syntology_url":"https://syntology.ai/paper/1608.03981","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1608.03981"}}}},{"paper":"/paper/deeply-recursive-convolutional-network-for","slug":"deeply-recursive-convolutional-network-for","title":"Deeply-Recursive Convolutional Network for Image Super-Resolution","date":"2015-11-14","arxiv_id":"1511.04491","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/deeply-recursive-convolutional-network-for#ran","syntology_url":"https://syntology.ai/paper/1511.04491","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.04491"}}}},{"paper":"/paper/accurate-image-super-resolution-using-very","slug":"accurate-image-super-resolution-using-very","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","date":"2015-11-14","arxiv_id":"1511.04587","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/accurate-image-super-resolution-using-very#ran","syntology_url":"https://syntology.ai/paper/1511.04587","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.04587"}}}},{"paper":"/paper/image-super-resolution-using-deep","slug":"image-super-resolution-using-deep","title":"Image Super-Resolution Using Deep Convolutional Networks","date":"2014-12-31","arxiv_id":"1501.00092","rows_on_this_dataset":1,"code_links":60,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":27,"samples_ran":21,"samples_constructed":0,"samples_ran_checked":17,"samples_ran_instrument_failed":4,"samples_unverified":6,"pointer_only_for_licence":7,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/image-super-resolution-using-deep#ran","syntology_url":"https://syntology.ai/paper/1501.00092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1501.00092"}}}}],"record_sha256":"74d0427a1d5b9302474f92b99f49b04cf523ea446f69e21f8a557fa820abb86c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}