Low-Light Stereo Image Enhancement

Abstract

Stereo cameras are now commonly used in more and more devices. Nevertheless, visually unpleasant images captured under low-light conditions hinder their practical application. As an initial attempt at low-light stereo image enhancement, we propose a novel Dual-View Enhancement Network (DVENet) based on the Retinex theory, which consists of two stages. The first stage estimates an illumination map to obtain a coarse enhancement result, which boosts the correlation of two views, while the second stage recovers details by integrating the information from two views to achieve fine image quality improvement with the guidance of the illumination map. To fully utilize the dual-view correlation, we further design a wavelet-based view transfer module to efficiently carry out multi-scale detail recovery. Then, we design an illumination-aware attention fusion module to exploit the complementarity between the fused features from two views and the single-view features. Experiments on both synthetic and real-world stereo datasets demonstrate the superiority of our proposed method over existing solutions.