Bidirectional Convolutional-GRU Integrated GAN for Unpaired Low-Light Image Enhancement

Abstract

Image enhancement is a widely researched area in the domain of computer vision, particularly image processing. Among the subdomains, low-light image enhancement (LLIE) receives considerable attention due to problems and challenges imposed by poor lighting conditions. As such, low-light images suffer from poor visibility, distorted colors, and loss of details, which limits their usability in many applications. The traditional methods have struggled to preserve such details and make the images susceptible to over-enhancement. Whereas, the learning-based techniques rely heavily on paired datasets for training. Therefore, we propose a Bidirectional Conv-GRU integrated GAN framework. Involving bidirectional Conv-GRU modules in our use-case enables the model to capture both local textures and long-range feature dependencies. Also, the use of unpaired datasets allows it to learn flexible and realistic mappings without the strict need for aligned image pairs. The results demonstrate the potency of our proposed work as compared to state-of-the-art methods.