Yang, WeipengGao, HongxiaZou, WenbinHuang, ShashaChen, HongshengMa, JianliangChaine, RaphaëlleDeng, ZhigangKim, Min H.2023-10-092023-10-0920231467-8659https://doi.org/10.1111/cgf.14960https://diglib.eg.org:443/handle/10.1111/cgf14960Low-light conditions often result in the presence of significant noise and artifacts in captured images, which can be further exacerbated during the image enhancement process, leading to a decrease in visual quality. This paper aims to present an effective low-light image enhancement model based on the variation Retinex model that successfully suppresses noise and artifacts while preserving image details. To achieve this, we propose a modified Bilateral Total Variation to better smooth out fine textures in the illuminance component while maintaining weak structures. Additionally, tensor sparse coding is employed as a regularization term to remove noise and artifacts from the reflectance component. Experimental results on extensive and challenging datasets demonstrate the effectiveness of the proposed method, exhibiting superior or comparable performance compared to state-ofthe- art approaches. Code, dataset and experimental results are available at https://github.com/YangWeipengscut/BTRetinex.CCS Concepts: Computing methodologies -> Image processing; Low-level-vision tasksComputing methodologiesImage processingLowlevelvision tasks10.1111/cgf.1496011 pages