Abstract: In the course of decomposing and enhancing the low-light images with Retinex model,it needs to manually adjust the parameters continuously to reach the optimal solution,which will reduce the efficiency of the entire process.In addition,existing low-light image enhancement methods based on Retinex fail to take both reflectance and illumination into account when perfor-ming image enhancement,and there are problems such as too much noise in the reflectance of low-light image,low brightness and not enough prominent details in the illumination.Aiming to solve these problems,a data-driven deep network is proposed to learn the decomposition and the enhancement of the low-light images,and the model parameters are learned through the end-to-end network training.The network firstly decomposes the low-light images into the reflectance and the illumination.Aiming at the problem of high noise in the reflectance,an improved denoising convolutional neural network model NDnCNN is used for denoising,and aiming at the problems of low brightness and not enough prominent details in the illumination,we introduce the convolutional block attention model CBAM to enhance the details and guide the network to modify the illumination.Finally,the denoised reflectance and the modified illumination are used for image reconstruction.Experimental results show that the enhanced low-light image is more photo-realistic with increased brightness,prominent details,rich information and low image distortion.

Key words: Convolutional block attention model, Convolutional neural network, Improved DnCNN model, Low-light image enhancement, Retinex theory