Pixel-Wise Gamma Correction Mapping for Low-Light Image Enhancement

Abstract

Low-light image enhancement aims to improve the visual quality of images captured under poor illumination and has caught much attention these years. However, existing low-light enhancement methods encounter many problems, e.g., they may not be robust to diverse low-light conditions or have to sacrifice computational efficiency for enhancement performance, which hinder their practical applications. To solve these problems, this paper proposes a novel enhancement method, called Pixel-Wise Gamma Correction Mapping (PWGCM), which combines our innovative pixel-wise Gamma Correction (GC) and deep learning. Compared with conventional GC, our pixel-wise GC is characterized by a set of gamma correction maps, which have the same size as the input image and are taken to replace the single global GC parameter of conventional GC. These gamma correction maps are generated from the low-light image input by a lightweight convolutional neural network at low computational cost. New no-reference loss functions are provided to train the network, ensuring reliable unsupervised learning. Furthermore, our PWGCM is enhanced by an iterative strategy, under which the low-light input image is iteratively enhanced based on the generated gamma correction maps and can yield visually pleasant results. Extensive experiments are done to compare our PWGCM with several state-of-the-art methods in terms of visual quality, efficiency, and auxiliary effects on high-level tasks. The comparison results confirm the superiority of our PWGCM.