{"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/low-light-image-enhancement-with-multi-stage","title":"Low-Light Image Enhancement with Multi-Stage Residue Quantization and Brightness-Aware Attention","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Yunlong Liu","Tao Huang","Weisheng Dong","Fangfang Wu","Xin Li","Guangming Shi"],"abstract":"    Low-light image enhancement (LLIE) aims to recover illumination and improve the visibility of low-light images. Conventional LLIE methods often produce poor results because they neglect the effect of noise interference. Deep learning-based LLIE methods focus on learning a mapping function between low-light images and normal-light images that outperforms conventional LLIE methods. However, most deep learning-based LLIE methods cannot yet fully exploit the guidance of auxiliary priors provided by normal-light images in the training dataset. In this paper, we propose a brightness-aware network with normal-light priors based on brightness-aware attention and residual quantized codebook. To achieve a more natural and realistic enhancement, we design a query module to obtain more reliable normal-light features and fuse them with lowlight features by a fusion branch. In addition, we propose a brightness-aware attention module to further retain the color consistency between the enhanced results and the normal-light images. Extensive experimental results on both real-captured and synthetic data show that our method outperforms existing state-of-the-art methods.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Liu_Low-Light_Image_Enhancement_with_Multi-Stage_Residue_Quantization_and_Brightness-Aware_Attention_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Liu_Low-Light_Image_Enhancement_with_Multi-Stage_Residue_Quantization_and_Brightness-Aware_Attention_ICCV_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":"low-light-image-enhancement-with-multi-stage","repo_url":"https://github.com/LiuYunlong99/RQ-LLIE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"low-light-image-enhancement","task_name":"Low-Light Image Enhancement"},{"task_slug":"quantization","task_name":"Quantization"}],"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}