{"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/look-up-table-compression-for-efficient-image","title":"Look-Up Table Compression for Efficient Image Restoration","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Yinglong Li","Jiacheng Li","Zhiwei Xiong"],"abstract":"    Look-Up Table (LUT) has recently gained increasing attention for restoring High-Quality (HQ) images from Low-Quality (LQ) observations thanks to its high computational efficiency achieved through a \"space for time\" strategy of caching learned LQ-HQ pairs. However incorporating multiple LUTs for improved performance comes at the cost of a rapidly growing storage size which is ultimately restricted by the allocatable on-device cache size. In this work we propose a novel LUT compression framework to achieve a better trade-off between storage size and performance for LUT-based image restoration models. Based on the observation that most cached LQ image patches are distributed along the diagonal of a LUT we devise a Diagonal-First Compression (DFC) framework where diagonal LQ-HQ pairs are preserved and carefully re-indexed to maintain the representation capacity while non-diagonal pairs are aggressively subsampled to save storage. Extensive experiments on representative image restoration tasks demonstrate that our DFC framework significantly reduces the storage size of LUT-based models (including our new design) while maintaining their performance. For instance DFC saves up to 90% of storage at a negligible performance drop for x4 super-resolution. The source code is available on GitHub: https://github.com/leenas233/DFC.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Li_Look-Up_Table_Compression_for_Efficient_Image_Restoration_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Li_Look-Up_Table_Compression_for_Efficient_Image_Restoration_CVPR_2024_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":"look-up-table-compression-for-efficient-image","repo_url":"https://github.com/leenas233/dfc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}