A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement

TL;DR

This paper introduces a bio-inspired multi-exposure fusion framework that enhances low-light images by accurately adjusting contrast and lightness, outperforming existing methods in reducing distortion.

Contribution

The paper proposes a novel dual-exposure fusion algorithm inspired by human vision, incorporating a new weight matrix and camera response model for improved low-light image enhancement.

Findings

Less contrast and lightness distortion compared to state-of-the-art methods

Effective synthesis of multi-exposure images using the proposed model

Enhanced images with better visibility and natural appearance

Abstract

Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. Although many image enhancement techniques have been proposed to solve this problem, existing methods inevitably introduce contrast under- and over-enhancement. Inspired by human visual system, we design a multi-exposure fusion framework for low-light image enhancement. Based on the framework, we propose a dual-exposure fusion algorithm to provide an accurate contrast and lightness enhancement. Specifically, we first design the weight matrix for image fusion using illumination estimation techniques. Then we introduce our camera response model to synthesize multi-exposure images. Next, we find the best exposure ratio so that the synthetic image is well-exposed in the regions where the original image is under-exposed. Finally, the enhanced result is obtained by fusing the input image and the synthetic image according to the weight matrix. Experiments show that our method can obtain results with less contrast and lightness distortion compared to that of several state-of-the-art methods.