UTrCGAN:Uncertainty-Driven Cycle-Consistent Generative Adversarial Network for Low-Light Image Enhancement

Low-light image enhancement is a computer vision task that aims to improve the visual perceptual quality of images captured in poorly illuminated scenes. At present, deep learning-based low-light enhancement methods can obtain high-quality enhanced images. However, it does not consider the statistical characteristics of different regions, such as edge, structure, and texture. The uncertainty of image regions is not well characterized and utilized. To address this problem, we propose a novel UnCertainty-driven Cycle-Consistent Generative Adversarial Network (UTrCGAN) to improve the performance of low-light enhancement. UTrCGAN first decomposes the unpaired low/normal-light images into reflectance and illumination components based on the Retinex theory. Then a generative adversarial network guided by uncertainty constraint is proposed to enhance the illumination component, in which the quality of the enhanced image is further improved by the guidance of variance estimation. Experimental results on the widely-used LOL dataset show that UTrCGAN outperforms the state-of-the-art methods in terms of visual quality and quantitative metrics.