Low-light image enhancement with geometrical sparse representation

Jin Tan, Taiping Zhang, Linchang Zhao, Darong Huang, Zhenyuan Zhang

Abstract: Low-light image enhancement (LLIE) can improve the visibility of low-light images. Low-light images exhibit a series of visual degradation, such as decreased visibility, low contrast, and biased colour, which hinder computer vision tasks. It is challenging to simultaneously handle these degradations. In this study, we propose a geometrical sparse representation (GSR) guided low-light enhancement model to fulfil the LLIE task. The proposed model consists of two branches to simultaneously learn the enhanced mapping of the low-light image and its GSR map. The main branch estimates the enhanced mapping of the low-light images through a generative adversarial network (GAN). Specifically, we introduce a novel structural model named the channel attention dense block (CADB) to the main branch for both efficiency and effectiveness. Moreover, there are always undesirable structural detail losses in the enhanced results. To remedy this shortcoming, we utilize a guided branch to learn the enhanced GSR map of the input low-light image and integrate it into the main branch as a structure prior. In addition, we propose a GSR-space GAN loss with GSR-space pixel loss to help generative networks concentrate more on geometrical structures. We conducted experiments on several benchmark datasets. The experimental results show that the proposed approach favourably performs against state-of-the-art, low-light image enhancement algorithms.