Single Image Low-Light Enhancement via a Dual-Path Generative Adversarial Network

Low-light enhancement plays an important role in overcoming image degradation and degraded performance of high-level computer vision tasks at night due to poorly illuminated environments. Existing low-light enhancement methods are often difficult to guarantee the accuracy of color and image details in complex low-light conditions. To solve the above problem, we design a low-light enhancement generative adversarial network with a dual-path design (DPGAN). We adopt a super-resolution subnetwork to provide high-gradient detail information of low-light images in one path, while a feature-forward network is designed in the other path. The feature-forward network consists of a feature extraction subnetwork and a feature refinement subnetwork; the former is designed for initial feature extraction, while the latter is then applied for deep refinement. Extensive experiments demonstrate the excellent performance of DPGAN in low-light image enhancement tasks.