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Images captured under low-light conditions are often plagued by severalchallenges, including diminished contrast, increased noise, loss of finedetails, and unnatural color reproduction. These factors can significantlyhinder the performance of computer vision tasks such as object detection andimage segmentation. As a result, improving the quality of low-light images isof paramount importance for practical applications in the computer visiondomain.To effectively address these challenges, we present a novel low-lightimage enhancement model, termed Spatial Consistency Retinex Network (SCRNet),which leverages the Retinex-based structure and is guided by the principle ofspatial consistency.Specifically, our proposed model incorporates three levelsof consistency: channel level, semantic level, and texture level, inspired bythe principle of spatial consistency.These levels of consistency enable ourmodel to adaptively enhance image features, ensuring more accurate and visuallypleasing results.Extensive experimental evaluations on various low-light imagedatasets demonstrate that our proposed SCRNet outshines existingstate-of-the-art methods, highlighting the potential of SCRNet as an effectivesolution for enhancing low-light images.

SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency