Combining attention mechanism and Retinex model to enhance low-light images

Abstract

Low-light image enhancement is challenging due to intractable problems such as color distortion and noise, which hide in the dark. Simply enhancing the brightness of dark areas will inevitably amplify hidden artifacts. We have observed more noise in the underexposed areas of images than in the normally exposed areas. Attention mechanism can be used to emphasize the vital information of the processed object and suppress some irrelevant information. Inspired by these observations, we propose a deep network that Combines Attention mechanism and Retinex (CA&R Net) model to enhance low-light images. Firstly, we develop an attention map to evaluate the degree of image underexposure and guide enhancement in a region-adaptive manner. This way, it can enhance underexposed areas and avoid over-enhancing normally exposed areas. Secondly, we use the reconstructed reflectance and low illumination to predict the illumination layers of the image jointly. This joint prediction utilizes the attention mechanism, making illumination adjustment achieve better results. The quantitative experimental results show that the CA&R Net can successfully handle noise, color distortion, and multiple types of degradation with the power of attention information. Moreover, both SSIM and PSNR are better than other advanced methods.