{"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/illumination-aware-image-quality-assessment","title":"Illumination-Aware Image Quality Assessment for Enhanced Low-light Image","arxiv_id":null,"date":"2021-10-22","proceeding":"PRCV 2021 2021 10","authors":["Sigan Yao，Yiqin Zhu，Lingyu Liang，Tao Wang"],"abstract":"Images captured in a dark environment may suffer from low visibility, which may degrade the visual aesthetics of images and the performance of vision-based systems. Extensive studies have focused on the low-light image enhancement (LIE) problem. However, we observe that even though the state-of-the-art LIE methods may oversharpen the low-light image and introduce visual artifacts. To reduce the overshoot effects of LIE, this paper proposes an illumination-aware image quality assessment, called LIE-IQA, for the enhanced low-light images. Since directly using the IQA of degraded image may fail to perform well, the proposed LIE-IQA is an illumination-aware and learnable metric. At first, the reflectance and shading components of both the enhanced low-light image and reference image are extracted by intrinsic image decomposition. Then, we use the weighted similarity between the VGG-based feature of enhanced low-light image and reference image to obtain LIE-IQA, where the weight of the measurement can be learned from pairs of data on benchmark dataset. Qualitative and quantitative experiments illustrate the superiority of the LIE-IQA to measure the image quality of LIE on different datasets, including a new IQA dataset built for LIE. We also use the LIE-IQA as a regularization of a loss function to optimize an end-to-end LIE method, and the results indicate the potential of the optimization framework with LIE-IQA to reduce the overshoot effects of low-light enhancement.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-030-88010-1_19","url_pdf":"https://link.springer.com/chapter/10.1007/978-3-030-88010-1_19","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":"illumination-aware-image-quality-assessment","repo_url":"https://github.com/MindCode-4/code-10/tree/main/LIE-IQA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"illumination-aware-image-quality-assessment","repo_url":"https://github.com/mindspore-ai/contrib","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"image-quality-assessment","task_name":"Image Quality Assessment"},{"task_slug":"intrinsic-image-decomposition","task_name":"Intrinsic Image Decomposition"},{"task_slug":"low-light-image-enhancement","task_name":"Low-Light Image Enhancement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}