{"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/toward-stable-interpretable-and-lightweight","title":"Toward Stable, Interpretable, and Lightweight Hyperspectral Super-Resolution","arxiv_id":null,"date":"2023-01-01","proceeding":"CVPR 2023 1","authors":["Weiying Xie","Kai Jiang","Yunsong Li","Jie Lei","Leyuan Fang","Wen-jin Guo"],"abstract":"    For real applications, existing HSI-SR methods are mostly not only limited to unstable performance under unknown scenarios but also suffer from high computation consumption. In this paper, we develop a new coordination optimization framework for stable, interpretable, and lightweight HSI-SR. Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework. The estimated degradation is applied to fusion as guidance for a degradation-aware HSI-SR. Under the framework, we establish an explicit degradation estimation method to tackle the indeterminacy and unstable performance driven by black-box simulation in previous methods. Considering the interpretability in fusion, we integrate spectral mixing prior to the fusion process, which can be easily realized by a tiny autoencoder, leading to a dramatic release of the computation burden. We then develop a partial fine-tune strategy in inference to reduce the computation cost further. Comprehensive experiments demonstrate the superiority of our method against state-of-the-art under synthetic and real datasets. For instance, we achieve a 2.3 dB promotion on PSNR with 120x model size reduction and 4300x FLOPs reduction under the CAVE dataset. Code is available in https://github.com/WenjinGuo/DAEM.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2023/html/Xie_Toward_Stable_Interpretable_and_Lightweight_Hyperspectral_Super-Resolution_CVPR_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2023/papers/Xie_Toward_Stable_Interpretable_and_Lightweight_Hyperspectral_Super-Resolution_CVPR_2023_paper.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":"toward-stable-interpretable-and-lightweight","repo_url":"https://github.com/wenjinguo/daem","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}