{"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/to-4d","entry":"to_4d","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":69,"n_papers_ran":66,"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":7,"n_samples_ran":4,"n_samples_fingerprinted":1,"n_places":69,"n_places_pointer_only":36,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":3,"ran":0,"unverified":3},"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":"2609.12497","paper":"/paper/arxiv-2609-12497","title":"RoES: Rotational Equivariant Selective-frequency Fusion for Multimodal Images","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"BryceLosky/RoES-Fusion","path":"models/modules/restormer.py","file_url":"https://github.com/BryceLosky/RoES-Fusion/blob/HEAD/models/modules/restormer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MPL-2.0","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2608.06184","paper":"/paper/arxiv-2608-06184","title":"EvReflection: Event-Driven Micro-Dynamics for Reflection Removal","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"JiaxiaoWang/EvReflection","path":"models/attn_util.py","file_url":"https://github.com/JiaxiaoWang/EvReflection/blob/HEAD/models/attn_util.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2603.18488","paper":"/paper/arxiv-2603-18488","title":"TexEditor: Structure-Preserving Text-Driven Texture Editing","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"KlingAIResearch/TexEditor","path":"model/sauge_vitb.py","file_url":"https://github.com/KlingAIResearch/TexEditor/blob/HEAD/model/sauge_vitb.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2509.15891","paper":"/paper/arxiv-2509-15891","title":"Global Regulation and Excitation via Attention Tuning for Stereo Matching","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"JarvisLee0423/GREAT-Stereo","path":"models/great_stereo/transformers.py","file_url":"https://github.com/JarvisLee0423/GREAT-Stereo/blob/HEAD/models/great_stereo/transformers.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e86e697a628c0575","mcp_get_code":{"code_sha256":"e86e697a628c0575"}},{"arxiv_id":"2507.05108","paper":"/paper/reviving-cultural-heritage-a-novel-approach","title":"Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration","date":"2025-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SCUT-DLVCLab/AutoHDR","path":"models/attention.py","file_url":"https://github.com/SCUT-DLVCLab/AutoHDR/blob/HEAD/models/attention.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2506.22246","paper":"/paper/eamamba-efficient-all-around-vision-state","title":"EAMamba: Efficient All-Around Vision State Space Model for Image Restoration","date":"2025-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daidaijr/EAMamba","path":"models/eamamba.py","file_url":"https://github.com/daidaijr/EAMamba/blob/HEAD/models/eamamba.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2506.21866","paper":null,"title":"arXiv:2506.21866","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"CSYSI/DPU-Former","path":"DPU-Former_25_IJCAI/lib/decoder.py","file_url":"https://github.com/CSYSI/DPU-Former/blob/HEAD/DPU-Former_25_IJCAI/lib/decoder.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2501.13312","paper":"/paper/tensor-var-variational-data-assimilation-in","title":"Tensor-Var: Variational Data Assimilation in Tensor Product Feature Space","date":"2025-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yyimingucl/TensorVar","path":"model/ERA5_model/transformer.py","file_url":"https://github.com/yyimingucl/TensorVar/blob/HEAD/model/ERA5_model/transformer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"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":"specialist_model/basicsr/archs/Restormer_arch.py","file_url":"https://github.com/House-Leo/FoundIR/blob/HEAD/specialist_model/basicsr/archs/Restormer_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2411.15922","paper":"/paper/prompthsi-universal-hyperspectral-image","title":"PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation","date":"2024-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chingheng0808/PromptHSI","path":"utils/modules.py","file_url":"https://github.com/chingheng0808/PromptHSI/blob/HEAD/utils/modules.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2411.02840","paper":"/paper/test-time-dynamic-image-fusion","title":"Test-Time Dynamic Image Fusion","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Yinan-Xia/TTD","path":"net.py","file_url":"https://github.com/Yinan-Xia/TTD/blob/HEAD/net.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2409.01686","paper":"/paper/frequency-spatial-entanglement-learning-for","title":"Frequency-Spatial Entanglement Learning for Camouflaged Object Detection","date":"2024-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CSYSI/FSEL","path":"FSEL_ECCV_2024/lib/FSEL_modules.py","file_url":"https://github.com/CSYSI/FSEL/blob/HEAD/FSEL_ECCV_2024/lib/FSEL_modules.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2408.13459","paper":"/paper/rethinking-video-deblurring-with-wavelet","title":"Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model","date":"2024-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chen-rao/vd-diff","path":"basicsr/archs/ChanDynamic_GMLP.py","file_url":"https://github.com/chen-rao/vd-diff/blob/HEAD/basicsr/archs/ChanDynamic_GMLP.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2408.08601","paper":"/paper/learning-a-low-level-vision-generalist-via","title":"Learning A Low-Level Vision Generalist via Visual Task Prompt","date":"2024-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chxy95/genlv","path":"conference_version/models_xrestormer_prompt_crossattn_wores.py","file_url":"https://github.com/chxy95/genlv/blob/HEAD/conference_version/models_xrestormer_prompt_crossattn_wores.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2408.08149","paper":"/paper/unsupervised-variational-translator-for","title":"Unsupervised Variational Translator for Bridging Image Restoration and High-Level Vision Tasks","date":"2024-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fire-friend/vat","path":"VaTrainer/network.py","file_url":"https://github.com/fire-friend/vat/blob/HEAD/VaTrainer/network.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2408.08091","paper":"/paper/hair-hypernetworks-based-all-in-one-image","title":"HAIR: Hypernetworks-based All-in-One Image Restoration","date":"2024-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"toummHus/HAIR","path":"net/HAIR.py","file_url":"https://github.com/toummHus/HAIR/blob/HEAD/net/HAIR.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"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":"network_level2.py","file_url":"https://github.com/kdhRick2222/Exposure-slot/blob/HEAD/network_level2.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2407.13987","paper":"/paper/realviformer-investigating-attention-for-real","title":"RealViformer: Investigating Attention for Real-World Video Super-Resolution","date":"2024-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuehan717/realviformer","path":"archs/realviformer_arch.py","file_url":"https://github.com/yuehan717/realviformer/blob/HEAD/archs/realviformer_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2407.04621","paper":"/paper/onerestore-a-universal-restoration-framework","title":"OneRestore: A Universal Restoration Framework for Composite Degradation","date":"2024-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"31052f33cc443e68","mcp_get_code":{"code_sha256":"31052f33cc443e68"}},{"arxiv_id":"2406.18079","paper":"/paper/mfdnet-multi-frequency-deflare-network-for","title":"MFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare Removal","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jiang-maomao/flare-removal","path":"models/model.py","file_url":"https://github.com/Jiang-maomao/flare-removal/blob/HEAD/models/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2406.16540","paper":"/paper/improving-robustness-to-corruptions-with","title":"Improving robustness to corruptions with multiplicative weight perturbations","date":"2024-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trungtrinh44/damp","path":"randaugment/augment.py","file_url":"https://github.com/trungtrinh44/damp/blob/HEAD/randaugment/augment.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bd1c72a9d8556505","mcp_get_code":{"code_sha256":"bd1c72a9d8556505"}},{"arxiv_id":"2405.19769","paper":"/paper/all-in-one-medical-image-restoration-via-task","title":"All-In-One Medical Image Restoration via Task-Adaptive Routing","date":"2024-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaziwel/all-in-one-medical-image-restoration-via-task-adaptive-routing","path":"model/Model_AMIR.py","file_url":"https://github.com/yaziwel/all-in-one-medical-image-restoration-via-task-adaptive-routing/blob/HEAD/model/Model_AMIR.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2405.14343","paper":"/paper/efficient-visual-state-space-model-for-image","title":"Efficient Visual State Space Model for Image Deblurring","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kkkls/evssm","path":"basicsr/models/archs/EVSSM_arch.py","file_url":"https://github.com/kkkls/evssm/blob/HEAD/basicsr/models/archs/EVSSM_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"31052f33cc443e68","mcp_get_code":{"code_sha256":"31052f33cc443e68"}},{"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/models/archs/IFA_arch.py","file_url":"https://github.com/YhuoyuH/RetinexMamba/blob/HEAD/basicsr/models/archs/IFA_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2404.16302","paper":"/paper/cfmw-cross-modality-fusion-mamba-for","title":"CFMW: Cross-modality Fusion Mamba for Multispectral Object Detection under Adverse Weather Conditions","date":"2024-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lhy-zjut/cfmw","path":"vim/mamba_module.py","file_url":"https://github.com/lhy-zjut/cfmw/blob/HEAD/vim/mamba_module.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2404.08406","paper":"/paper/mambadfuse-a-mamba-based-dual-phase-model-for","title":"MambaDFuse: A Mamba-based Dual-phase Model for Multi-modality Image Fusion","date":"2024-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2404.04785","paper":"/paper/rethinking-diffusion-model-for-multi-contrast","title":"Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution","date":"2024-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guangyuankk/diffmsr","path":"DiffMSR_Main/archs/CATL.py","file_url":"https://github.com/guangyuankk/diffmsr/blob/HEAD/DiffMSR_Main/archs/CATL.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2404.02154","paper":"/paper/dynamic-pre-training-towards-efficient-and","title":"Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akshaydudhane16/dynet","path":"net/DyNet_large.py","file_url":"https://github.com/akshaydudhane16/dynet/blob/HEAD/net/DyNet_large.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2404.01547","paper":"/paper/bidirectional-multi-scale-implicit-neural","title":"Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cschenxiang/nerd-rain","path":"model.py","file_url":"https://github.com/cschenxiang/nerd-rain/blob/HEAD/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2403.16387","paper":"/paper/text-if-leveraging-semantic-text-guidance-for","title":"Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and Interactive Image Fusion","date":"2024-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xunpengyi/text-if","path":"model/Text_IF_model.py","file_url":"https://github.com/xunpengyi/text-if/blob/HEAD/model/Text_IF_model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2403.14614","paper":"/paper/adair-adaptive-all-in-one-image-restoration","title":"AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"c-yn/adair","path":"net/model.py","file_url":"https://github.com/c-yn/adair/blob/HEAD/net/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2403.10067","paper":"/paper/hybrid-convolutional-and-attention-network","title":"Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"summitgao/hcanet","path":"HCANet.py","file_url":"https://github.com/summitgao/hcanet/blob/HEAD/HCANet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2403.08330","paper":"/paper/activating-wider-areas-in-image-super","title":"Activating Wider Areas in Image Super-Resolution","date":"2024-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arsenalcheng/mma","path":"basicsr/archs/mlpmixer_util.py","file_url":"https://github.com/arsenalcheng/mma/blob/HEAD/basicsr/archs/mlpmixer_util.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2402.18172","paper":"/paper/nitedr-nighttime-image-de-raining-with-cross","title":"NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes","date":"2024-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2402.12192","paper":"/paper/pan-mamba-effective-pan-sharpening-with-state","title":"Pan-Mamba: Effective pan-sharpening with State Space Model","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexhe101/pan-mamba","path":"pan-sharpening/model/mamba_module.py","file_url":"https://github.com/alexhe101/pan-mamba/blob/HEAD/pan-sharpening/model/mamba_module.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2401.15583","paper":"/paper/sctransnet-spatial-channel-cross-transformer","title":"SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection","date":"2024-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xdfai/sctransnet","path":"model/SCTransNet.py","file_url":"https://github.com/xdfai/sctransnet/blob/HEAD/model/SCTransNet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2401.05907","paper":"/paper/efficient-image-deblurring-networks-based-on","title":"Efficient Image Deblurring Networks based on Diffusion Models","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bnm6900030/swintormer","path":"basicsr/archs/Swintormer_arch.py","file_url":"https://github.com/bnm6900030/swintormer/blob/HEAD/basicsr/archs/Swintormer_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2312.15736","paper":"/paper/towards-real-world-blind-face-restoration-1","title":"Towards Real-World Blind Face Restoration with Generative Diffusion Prior","date":"2023-12-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenxx89/bfrffusion","path":"models/transformerBlock.py","file_url":"https://github.com/chenxx89/bfrffusion/blob/HEAD/models/transformerBlock.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2312.05038","paper":"/paper/prompt-in-prompt-learning-for-universal-image","title":"Prompt-In-Prompt Learning for Universal Image Restoration","date":"2023-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"longzilicart/pip_universal","path":"src/net/PIP_utils.py","file_url":"https://github.com/longzilicart/pip_universal/blob/HEAD/src/net/PIP_utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2311.11638","paper":"/paper/reti-diff-illumination-degradation-image","title":"Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model","date":"2023-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chunminghe/reti-diff","path":"Reti-Diff/archs/S1_arch.py","file_url":"https://github.com/chunminghe/reti-diff/blob/HEAD/Reti-Diff/archs/S1_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2311.11600","paper":"/paper/deep-equilibrium-diffusion-restoration-with","title":"Deep Equilibrium Diffusion Restoration with Parallel Sampling","date":"2023-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caojiezhang/deqir","path":"models/restormer_arch.py","file_url":"https://github.com/caojiezhang/deqir/blob/HEAD/models/restormer_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2310.12848","paper":"/paper/neural-degradation-representation-learning","title":"Neural Degradation Representation Learning for All-In-One Image Restoration","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mdyao/NDR-Restore","path":"models/modules/NDR.py","file_url":"https://github.com/mdyao/NDR-Restore/blob/HEAD/models/modules/NDR.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2310.11881","paper":"/paper/a-comparative-study-of-image-restoration","title":"A Comparative Study of Image Restoration Networks for General Backbone Network Design","date":"2023-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"andrew0613/x-restormer","path":"xrestormer/archs/restormer_base_arch.py","file_url":"https://github.com/andrew0613/x-restormer/blob/HEAD/xrestormer/archs/restormer_base_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2310.01840","paper":"/paper/self-supervised-high-dynamic-range-imaging","title":"Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cszhilu1998/SelfHDR","path":"models/sctnet.py","file_url":"https://github.com/cszhilu1998/SelfHDR/blob/HEAD/models/sctnet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"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/models/archs/MB_TaylorFormer.py","file_url":"https://github.com/fvl2020/iccv-2023-mb-taylorformer/blob/HEAD/basicsr/models/archs/MB_TaylorFormer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2308.11932","paper":"/paper/synergistic-multiscale-detail-refinement-via","title":"Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement","date":"2023-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhoujingchun03/smdr-is","path":"model/encoder_list.py","file_url":"https://github.com/zhoujingchun03/smdr-is/blob/HEAD/model/encoder_list.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2308.10820","paper":"/paper/pixel-adaptive-deep-unfolding-transformer-for","title":"Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image Reconstruction","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"myuli/padut","path":"real/test_code/architecture/padut.py","file_url":"https://github.com/myuli/padut/blob/HEAD/real/test_code/architecture/padut.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2305.19141","paper":"/paper/taylorformer-probabilistic-predictions-for","title":"Taylorformer: Probabilistic Modelling for Random Processes including Time Series","date":"2023-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2305.17863","paper":"/paper/gridformer-residual-dense-transformer-with","title":"GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions","date":"2023-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taowangzj/gridformer","path":"basicsr/archs/GridFormer_arch.py","file_url":"https://github.com/taowangzj/gridformer/blob/HEAD/basicsr/archs/GridFormer_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2305.12966","paper":"/paper/hierarchical-integration-diffusion-model-for-1","title":"Hierarchical Integration Diffusion Model for Realistic Image Deblurring","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengchen1999/HI-Diff","path":"hi_diff/archs/Transformer_arch.py","file_url":"https://github.com/zhengchen1999/HI-Diff/blob/HEAD/hi_diff/archs/Transformer_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2305.11443","paper":"/paper/equivariant-multi-modality-image-fusion","title":"Equivariant Multi-Modality Image Fusion","date":"2023-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2304.00534","paper":"/paper/lg-bpn-local-and-global-blind-patch-network","title":"LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising","date":"2023-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2303.06440","paper":"/paper/xformer-hybrid-x-shaped-transformer-for-image","title":"Xformer: Hybrid X-Shaped Transformer for Image Denoising","date":"2023-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gladzhang/Xformer","path":"basicsr/models/archs/Xformer_arch.py","file_url":"https://github.com/gladzhang/Xformer/blob/HEAD/basicsr/models/archs/Xformer_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2212.11548","paper":"/paper/ultra-high-definition-low-light-image","title":"Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method","date":"2022-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2211.14461","paper":"/paper/cddfuse-correlation-driven-dual-branch","title":"CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion","date":"2022-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2211.12250","paper":"/paper/efficient-frequency-domain-based-transformers","title":"Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring","date":"2022-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2111.09881","paper":"/paper/restormer-efficient-transformer-for-high","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","date":"2021-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"2004.01461","paper":"/paper/gradient-centralization-a-new-optimization","title":"Gradient Centralization: A New Optimization Technique for Deep Neural Networks","date":"2020-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HamadYA/GhostFaceNets","path":"augment.py","file_url":"https://github.com/HamadYA/GhostFaceNets/blob/HEAD/augment.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bd1c72a9d8556505","mcp_get_code":{"code_sha256":"bd1c72a9d8556505"}},{"arxiv_id":"2003.06792","paper":"/paper/learning-enriched-features-for-real-image","title":"Learning Enriched Features for Real Image Restoration and Enhancement","date":"2020-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"1911.04252","paper":"/paper/self-training-with-noisy-student-improves","title":"Self-training with Noisy Student improves ImageNet classification","date":"2019-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leondgarse/Keras_efficientnet_v2_test","path":"keras_efficientnet_v2/augment.py","file_url":"https://github.com/leondgarse/Keras_efficientnet_v2_test/blob/HEAD/keras_efficientnet_v2/augment.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"bd1c72a9d8556505","mcp_get_code":{"code_sha256":"bd1c72a9d8556505"}},{"arxiv_id":"1612.04642","paper":"/paper/harmonic-networks-deep-translation-and","title":"Harmonic Networks: Deep Translation and Rotation Equivariance","date":"2016-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deworrall92/harmonicConvolutions","path":"BSD500/BSD_model.py","file_url":"https://github.com/deworrall92/harmonicConvolutions/blob/HEAD/BSD500/BSD_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"31167f7af878ba77","mcp_get_code":{"code_sha256":"31167f7af878ba77"}},{"arxiv_id":"openreview_eDlsO4kFaX","paper":null,"title":"arXiv:openreview_eDlsO4kFaX","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"KiiSooo/CODiff","path":"model/net.py","file_url":"https://github.com/KiiSooo/CODiff/blob/HEAD/model/net.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4487a4c12288318","mcp_get_code":{"code_sha256":"b4487a4c12288318"}},{"arxiv_id":"aaai_28342","paper":null,"title":"arXiv:aaai_28342","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VUT-HFUT/EulerMormer","path":"models/magnet.py","file_url":"https://github.com/VUT-HFUT/EulerMormer/blob/HEAD/models/magnet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"aaai_28193","paper":null,"title":"arXiv:aaai_28193","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"CXH-Research/DeVigNet","path":"models/blocks.py","file_url":"https://github.com/CXH-Research/DeVigNet/blob/HEAD/models/blocks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad4674d96bb6fcef","mcp_get_code":{"code_sha256":"ad4674d96bb6fcef"}},{"arxiv_id":"aaai_27923","paper":null,"title":"arXiv:aaai_27923","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Jiahuiqu/M2DTN","path":"model.py","file_url":"https://github.com/Jiahuiqu/M2DTN/blob/HEAD/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"arxiv_id":"aaai_25369","paper":null,"title":"arXiv:aaai_25369","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"nana01219/GeoDSR","path":"models/GASA.py","file_url":"https://github.com/nana01219/GeoDSR/blob/HEAD/models/GASA.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"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":"basicsr/models/archs/restormer_arch.py","file_url":"https://github.com/swz30/Restormer/blob/HEAD/basicsr/models/archs/restormer_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"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":"deblur_model.py","file_url":"https://github.com/XinanXie/EGDeblurring/blob/HEAD/deblur_model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}},{"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/models/archs/fftformer_arch.py","file_url":"https://github.com/kkkls/FFTformer/blob/HEAD/basicsr/models/archs/fftformer_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f2a5df739a59e","mcp_get_code":{"code_sha256":"b20f2a5df739a59e"}}]}