{"url":"/task/spectral-reconstruction","name":"Spectral Reconstruction","slug":"spectral-reconstruction","description_markdown":null,"categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":72,"papers_with_code":37,"benchmarks":4,"benchmark_tables_in_archive":4,"benchmark_tables_shown":4,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":4,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/spectral-reconstruction-on-arad-1k","slug":"spectral-reconstruction-on-arad-1k","dataset":"ARAD-1K","dataset_url":"/dataset/arad-1k","rows_in_archive":11,"metrics":["PSNR","MRAE","RMSE"],"first_row_in_archive_order":{"model":"MST++","paper_title":"MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction","paper_url":"/paper/mst-multi-stage-spectral-wise-transformer-for","paper_date":"2022-04-17","arxiv_id":"2204.07908","code_links":[{"title":"cmhungsteve/Awesome-Transformer-Attention","url":"https://github.com/cmhungsteve/Awesome-Transformer-Attention"},{"title":"caiyuanhao1998/MST","url":"https://github.com/caiyuanhao1998/MST"},{"title":"caiyuanhao1998/MST-plus-plus","url":"https://github.com/caiyuanhao1998/MST-plus-plus"}],"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}}},{"leaderboard":"/sota/spectral-reconstruction-on-cave","slug":"spectral-reconstruction-on-cave","dataset":"CAVE","dataset_url":"/dataset/cave","rows_in_archive":10,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"SSR","paper_title":"Improving Spectral Snapshot Reconstruction with Spectral-Spatial Rectification","paper_url":"/paper/improving-spectral-snapshot-reconstruction","paper_date":"2024-01-01","arxiv_id":null,"code_links":[{"title":"zhangjc-2k/ssr","url":"https://github.com/zhangjc-2k/ssr"}],"syntology":null}},{"leaderboard":"/sota/spectral-reconstruction-on-kaist","slug":"spectral-reconstruction-on-kaist","dataset":"KAIST","dataset_url":"/dataset/kaist","rows_in_archive":10,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"SSR","paper_title":"Improving Spectral Snapshot Reconstruction with Spectral-Spatial Rectification","paper_url":"/paper/improving-spectral-snapshot-reconstruction","paper_date":"2024-01-01","arxiv_id":null,"code_links":[{"title":"zhangjc-2k/ssr","url":"https://github.com/zhangjc-2k/ssr"}],"syntology":null}},{"leaderboard":"/sota/spectral-reconstruction-on-real-hsi","slug":"spectral-reconstruction-on-real-hsi","dataset":"Real HSI","dataset_url":"/dataset/real-hsi","rows_in_archive":9,"metrics":["User Study Score"],"first_row_in_archive_order":{"model":"DAUHST-9stg","paper_title":"Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging","paper_url":"/paper/degradation-aware-unfolding-half-shuffle","paper_date":"2022-05-20","arxiv_id":"2205.10102","code_links":[{"title":"caiyuanhao1998/MST","url":"https://github.com/caiyuanhao1998/MST"}],"syntology":{"n":8,"n_ran":4,"n_unverified":4,"n_pointer_only":0}}}],"datasets":[{"url":"/dataset/cave","name":"CAVE","full_name":"Multispectral imaging using multiplexed illumination.","num_papers_in_archive":31},{"url":"/dataset/kaist","name":"KAIST","full_name":"High-quality hyperspectral reconstruction using a spectral prior","num_papers_in_archive":28},{"url":"/dataset/arad-1k","name":"ARAD-1K","full_name":"Ntire 2022 spectral recovery challenge and data set","num_papers_in_archive":14},{"url":"/dataset/real-hsi","name":"Real HSI","full_name":"End-to-End Low Cost Compressive Spectral Imaging with Spatial-Spectral Self-Attention","num_papers_in_archive":13}],"subtasks":[],"parent_tasks":[{"url":"/task/image-restoration","name":"Image Restoration"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":37,"tagged_in_all":72,"items":[{"url":"/paper/enhanced-deep-residual-networks-for-single","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","date":"2017-07-10","arxiv_id":"1707.02921","repositories_listed":45,"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":2}},{"url":"/paper/restormer-efficient-transformer-for-high","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","date":"2021-11-18","arxiv_id":"2111.09881","repositories_listed":13,"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":4}},{"url":"/paper/learning-enriched-features-for-real-image","title":"Learning Enriched Features for Real Image Restoration and Enhancement","date":"2020-03-15","arxiv_id":"2003.06792","repositories_listed":12,"syntology":{"n":20,"n_ran":3,"n_unverified":17,"n_pointer_only":3}},{"url":"/paper/multi-stage-progressive-image-restoration","title":"Multi-Stage Progressive Image Restoration","date":"2021-02-04","arxiv_id":"2102.02808","repositories_listed":8,"syntology":{"n":26,"n_ran":18,"n_unverified":8,"n_pointer_only":25}},{"url":"/paper/mask-guided-spectral-wise-transformer-for","title":"Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction","date":"2021-11-15","arxiv_id":"2111.07910","repositories_listed":4,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/mst-multi-stage-spectral-wise-transformer-for","title":"MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction","date":"2022-04-17","arxiv_id":"2204.07908","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}},{"url":"/paper/hdnet-high-resolution-dual-domain-learning","title":"HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging","date":"2022-03-04","arxiv_id":"2203.02149","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/hinet-half-instance-normalization-network-for","title":"HINet: Half Instance Normalization Network for Image Restoration","date":"2021-05-13","arxiv_id":"2105.06086","repositories_listed":2,"syntology":null},{"url":"/paper/stylemelgan-an-efficient-high-fidelity","title":"StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization","date":"2020-11-03","arxiv_id":"2011.01557","repositories_listed":2,"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/unmix-nerf-spectral-unmixing-meets-neural","title":"UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields","date":"2025-06-27","arxiv_id":"2506.21884","repositories_listed":1,"syntology":null},{"url":"/paper/model-guided-network-with-cluster-based","title":"Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-Resolution","date":"2025-05-30","arxiv_id":"2505.24605","repositories_listed":1,"syntology":null},{"url":"/paper/mp-hsir-a-multi-prompt-framework-for","title":"MP-HSIR: A Multi-Prompt Framework for Universal Hyperspectral Image Restoration","date":"2025-03-12","arxiv_id":"2503.09131","repositories_listed":1,"syntology":null},{"url":"/paper/gmsr-gradient-guided-mamba-for-spectral","title":"GMSR:Gradient-Guided Mamba for Spectral Reconstruction from RGB Images","date":"2024-05-13","arxiv_id":"2405.07777","repositories_listed":1,"syntology":null},{"url":"/paper/dual-prior-unfolding-for-snapshot-compressive","title":"Dual Prior Unfolding for Snapshot Compressive Imaging","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-spectral-snapshot-reconstruction","title":"Improving Spectral Snapshot Reconstruction with Spectral-Spatial Rectification","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learned-regularization-for-inverse-problems","title":"Learned Regularization for Inverse Problems: Insights from a Spectral Model","date":"2023-12-15","arxiv_id":"2312.09845","repositories_listed":1,"syntology":null},{"url":"/paper/spec-nerf-multi-spectral-neural-radiance","title":"Spec-NeRF: Multi-spectral Neural Radiance Fields","date":"2023-09-14","arxiv_id":"2310.12987","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_unverified":3,"n_pointer_only":16}},{"url":"/paper/rdfnet-regional-dynamic-fista-net-for","title":"RDFNet: Regional Dynamic FISTA-Net for Spectral Snapshot Compressive Imaging","date":"2023-02-06","arxiv_id":"2302.02519","repositories_listed":1,"syntology":null},{"url":"/paper/a-synthetic-hyperspectral-array-video","title":"Synthetic Hyperspectral Array Video Database with Applications to Cross-Spectral Reconstruction and Hyperspectral Video Coding","date":"2023-01-18","arxiv_id":"2301.07551","repositories_listed":1,"syntology":null},{"url":"/paper/residual-degradation-learning-unfolding","title":"Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging","date":"2022-11-13","arxiv_id":"2211.06891","repositories_listed":1,"syntology":null},{"url":"/paper/degradation-aware-unfolding-half-shuffle","title":"Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging","date":"2022-05-20","arxiv_id":"2205.10102","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/real-time-hyperspectral-imaging-in-hardware","title":"Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders","date":"2022-04-05","arxiv_id":"2204.02084","repositories_listed":1,"syntology":null},{"url":"/paper/coarse-to-fine-sparse-transformer-for","title":"Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction","date":"2022-03-09","arxiv_id":"2203.04845","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/adjust-a-dictionary-based-joint","title":"ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography","date":"2021-12-21","arxiv_id":"2112.11406","repositories_listed":1,"syntology":null},{"url":"/paper/reconstructing-spectral-functions-via","title":"Reconstructing spectral functions via automatic differentiation","date":"2021-11-29","arxiv_id":"2111.14760","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/deep-low-dimensional-spectral-image","title":"Deep Low-Dimensional Spectral Image Representation for Compressive Spectral Reconstruction","date":"2021-11-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semantic-embedded-unsupervised-spectral","title":"Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild","date":"2021-08-15","arxiv_id":"2108.06659","repositories_listed":1,"syntology":null},{"url":"/paper/deep-amended-gradient-descent-for-efficient","title":"Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images","date":"2021-08-12","arxiv_id":"2108.05547","repositories_listed":1,"syntology":null},{"url":"/paper/audio-spectral-enhancement-leveraging","title":"Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio Sequences","date":"2021-08-08","arxiv_id":"2108.03703","repositories_listed":1,"syntology":null},{"url":"/paper/spectral-reconstruction-and-disparity-from","title":"Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning","date":"2021-03-18","arxiv_id":"2103.10179","repositories_listed":1,"syntology":null}],"syntology_records":12,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","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)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}