{"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/estimate-aggd-param","entry":"estimate_aggd_param","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":9,"n_papers_ran":8,"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":2,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":1},"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":"2412.01427","paper":"/paper/foundir-unleashing-million-scale-training","title":"FoundIR: Unleashing Million-scale Training Data to Advance Foundation Models for Image Restoration","date":"2024-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"House-Leo/FoundIR","path":"metrics/niqe.py","file_url":"https://github.com/House-Leo/FoundIR/blob/HEAD/metrics/niqe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1282a48b0941f5d","mcp_get_code":{"code_sha256":"a1282a48b0941f5d"}},{"arxiv_id":"2409.01274","paper":"/paper/davide-depth-aware-video-deblurring","title":"DAVIDE: Depth-Aware Video Deblurring","date":"2024-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"germanftv/DAVIDE-Benckmark","path":"basicsr/metrics/niqe.py","file_url":"https://github.com/germanftv/DAVIDE-Benckmark/blob/HEAD/basicsr/metrics/niqe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1282a48b0941f5d","mcp_get_code":{"code_sha256":"a1282a48b0941f5d"}},{"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":"basicsr/metrics/niqe.py","file_url":"https://github.com/YhuoyuH/RetinexMamba/blob/HEAD/basicsr/metrics/niqe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1282a48b0941f5d","mcp_get_code":{"code_sha256":"a1282a48b0941f5d"}},{"arxiv_id":"2401.15235","paper":"/paper/cascadedgaze-efficiency-in-global-context","title":"CascadedGaze: Efficiency in Global Context Extraction for Image Restoration","date":"2024-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ascend-Research/CascadedGaze","path":"basicsr/metrics/niqe.py","file_url":"https://github.com/Ascend-Research/CascadedGaze/blob/HEAD/basicsr/metrics/niqe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1282a48b0941f5d","mcp_get_code":{"code_sha256":"a1282a48b0941f5d"}},{"arxiv_id":"2401.00027","paper":"/paper/efficient-multi-scale-network-with-learnable","title":"Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring","date":"2023-12-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thqiu0419/mlwnet","path":"basicsr/metrics/niqe.py","file_url":"https://github.com/thqiu0419/mlwnet/blob/HEAD/basicsr/metrics/niqe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a1282a48b0941f5d","mcp_get_code":{"code_sha256":"a1282a48b0941f5d"}},{"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":"basicsr/metrics/niqe.py","file_url":"https://github.com/fvl2020/iccv-2023-mb-taylorformer/blob/HEAD/basicsr/metrics/niqe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a1282a48b0941f5d","mcp_get_code":{"code_sha256":"a1282a48b0941f5d"}},{"arxiv_id":"2204.04676","paper":"/paper/simple-baselines-for-image-restoration","title":"Simple Baselines for Image Restoration","date":"2022-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"murufeng/FUIR","path":"basicsr/metrics/niqe.py","file_url":"https://github.com/murufeng/FUIR/blob/HEAD/basicsr/metrics/niqe.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":"a1282a48b0941f5d","mcp_get_code":{"code_sha256":"a1282a48b0941f5d"}},{"arxiv_id":"Kong_Efficient_Frequency_Domain-Based_Transformers_for_High-Quality_Image_Deblurring_CVPR_2023_paper","paper":null,"title":"arXiv:Kong_Efficient_Frequency_Domain-Based_Transformers_for_High-Quality_Image_Deblurring_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"kkkls/FFTformer","path":"basicsr/metrics/niqe.py","file_url":"https://github.com/kkkls/FFTformer/blob/HEAD/basicsr/metrics/niqe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1282a48b0941f5d","mcp_get_code":{"code_sha256":"a1282a48b0941f5d"}},{"arxiv_id":"Chao_Equivalent_Transformation_and_Dual_Stream_Network_Construction_for_Mobile_Image_CVPR_2023_paper","paper":null,"title":"arXiv:Chao_Equivalent_Transformation_and_Dual_Stream_Network_Construction_for_Mobile_Image_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ECNUSR/ETDS","path":"core/criterions/niqe.py","file_url":"https://github.com/ECNUSR/ETDS/blob/HEAD/core/criterions/niqe.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":"3f4dc8530f81bda4","mcp_get_code":{"code_sha256":"3f4dc8530f81bda4"}}]}