{"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":"/paper/hdnet-high-resolution-dual-domain-learning","title":"HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging","arxiv_id":"2203.02149","date":"2022-03-04","proceeding":"CVPR 2022 1","authors":["Xiaowan Hu","Yuanhao Cai","Jing Lin","Haoqian Wang","Xin Yuan","Yulun Zhang","Radu Timofte","Luc van Gool"],"abstract":"The rapid development of deep learning provides a better solution for the end-to-end reconstruction of hyperspectral image (HSI). However, existing learning-based methods have two major defects. Firstly, networks with self-attention usually sacrifice internal resolution to balance model performance against complexity, losing fine-grained high-resolution (HR) features. Secondly, even if the optimization focusing on spatial-spectral domain learning (SDL) converges to the ideal solution, there is still a significant visual difference between the reconstructed HSI and the truth. Therefore, we propose a high-resolution dual-domain learning network (HDNet) for HSI reconstruction. On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features. On the other hand, frequency domain learning (FDL) is introduced for HSI reconstruction to narrow the frequency domain discrepancy. Dynamic FDL supervision forces the model to reconstruct fine-grained frequencies and compensate for excessive smoothing and distortion caused by pixel-level losses. The HR pixel-level attention and frequency-level refinement in our HDNet mutually promote HSI perceptual quality. Extensive quantitative and qualitative evaluation experiments show that our method achieves SOTA performance on simulated and real HSI datasets. Code and models will be released at https://github.com/caiyuanhao1998/MST","url_abs":"https://arxiv.org/abs/2203.02149v2","url_pdf":"https://arxiv.org/pdf/2203.02149v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hdnet-high-resolution-dual-domain-learning","repo_url":"https://github.com/caiyuanhao1998/MST","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"hdnet-high-resolution-dual-domain-learning","repo_url":"https://github.com/caiyuanhao1998/MST-plus-plus","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"compressive-sensing","task_name":"Compressive Sensing"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"spectral-reconstruction","task_name":"Spectral Reconstruction"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/spectral-reconstruction-on-arad-1k","task":"Spectral Reconstruction","dataset":"ARAD-1K","model":"HDNet","rank_in_archive_order":7,"of":11,"metrics":{"MRAE":"0.2048","PSNR":"32.13","RMSE":"0.0317"},"uses_additional_data":false},{"leaderboard":"/sota/spectral-reconstruction-on-cave","task":"Spectral Reconstruction","dataset":"CAVE","model":"HDNet","rank_in_archive_order":9,"of":10,"metrics":{"PSNR":"34.97","SSIM":"0.943"},"uses_additional_data":false},{"leaderboard":"/sota/spectral-reconstruction-on-kaist","task":"Spectral Reconstruction","dataset":"KAIST","model":"HDNet","rank_in_archive_order":9,"of":10,"metrics":{"PSNR":"34.97","SSIM":"0.943"},"uses_additional_data":false},{"leaderboard":"/sota/spectral-reconstruction-on-real-hsi","task":"Spectral Reconstruction","dataset":"Real HSI","model":"HDNet","rank_in_archive_order":7,"of":9,"metrics":{"User Study Score":"11"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.02149","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02149"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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