Images captured in low-light environments often suffer from various complex degradations. The main task of low-light image enhancement (LLIE) is to improve the brightness and restore the details of the image, thereby producing images that are more in line with human perception. Retinex-based methods decompose images into illumination and reflectance components, and enhance images by adjusting illumination and restoring reflectance details. However, the presence of noise in images usually leads to inaccurate decomposition, producing results that deviate from realistic appearances. In order to prevent the influence of noise on the subsequent decomposition tasks, we first denoise the low-light images, perform Retinex decomposition on the denoised images, and try to establish paired low-light images to maintain the consistency of the reflectance components. Specifically, we propose a Clean Decomposition Network (CDNet), an unsupervised learning method that first performs denoising on low-light image pairs to prevent loss of details and color deviation, and then performs Retinex decomposition, and guides network optimization through a carefully designed denoising network and loss function. Extensive experiments on eight public datasets, including LOL and SICE, demonstrate that our method achieves superior enhancement performance, is highly competitive with state-of-the-art unsupervised methods, and achieves comparable levels to supervised methods on certain metrics and datasets.
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