Recurrent Attentive Decomposition Network for Low-Light Image Enhancement

Abstract

This paper aims to solve the problems of Low-light image enhancement based on classical method RetinexNet. Given the problems of original results with lots of noise and color distortion, this paper proposes a novel recurrent attentive decomposition network, which combines spatial attention mechanism and Encoder-Decoder structure to better capture the key information of images and make a thorough image decomposition process. Furthermore, another network based on attention mechanism is added to denoise the reflection image and improve the restoration effect of image details. Compared with RetinexNet and other popular methods, the overall style of images processed by our method is more consistent with that of the real scene. Both visual comparison and quantity comparison of Structural Similarity(SSIM) and Peak Signal to Noise Ratio(PSNR) demonstrate that our method is with superiority to several state-of-the-art methods.