Low-light image enhancement based on simulated multi-exposure fusion

Abstract We propose an efficient and novel framework for low-light image enhancement, which aims to reveal information hidden in the darkness and improve overall brightness and local contrast. Inspired by exposure fusion technique, we employ simulated multi-exposure images fusion to derive bright, natural and satisfactory results, while images are taken under poor conditions such as insufficient or uneven illumination, back-lit and limited exposure time. Specifically, we first design a novel method to generate synthesized images with varying exposure time from a single image. Thus, each image of these artificial sequences contains necessary information for the final desired enhanced result. We then introduce a flexible multi-exposure fusion framework to achieve fused images, which comprises a weight map prediction module and a multi-scale fusion module. Extensive experiments show that our approach can achieve similar or better performance compared to serval state-of-the-art methods.