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Low-light Image Enhancement based on Joint Decomposition and Denoising U-Net Network

In the process of low-light image enhancement, how to handle the noise effectively is an important yet challenging problem. Based on Retinex decomposition theory, this paper proposes a deep learning method for low-light image enhancement with joint decomposition and denoising. First, we propose a new joint decomposition and denoising enhancement U-Net network (JDEU). The network can be trained with low-light images only, and high quality normal-light images are used as reference guided decomposition network to decompose the desired reflectance component for noise removal. Then, the decomposition result is used as the input of the enhanced network to adjust the illumination component brightness. Finally, the reflectance and the adjusted illumination are reconstructed to produce the enhanced image. Experiments based on LOL dataset show that our method not only performs well in texture details and denoising, but also reduces color distortion and greatly improves the overall visual effect.