RetinexGAN: A Hybrid Approach for Low-Light Image Enhancement

Low-light image enhancement is a significant challenge in computational imaging, necessitating innovative solutions that maintain visual information under suboptimal illumination.This research presents a novel hybrid methodology that integrates RetinexNet decomposition with a Generative Adversarial Network (GAN)-based enhancement model, effectively addressing key limitations in existing low-light image processing techniques.By decomposing images into reflectance and illumination components using an advanced deep learning framework, our approach develops a comprehensive strategy for reconstructing high-quality images.The method emphasizes the effective separation and enhancement of image components while preserving structural integrity and perceptual consistency.This two-stage enhancement process merges Retinex theory with sophisticated neural network architectures, exploring innovative strategies to transform images captured in challenging lighting conditions.Our methodology aims to overcome traditional challenges in low-light imaging, including noise amplification, color distortion, and loss of structural details.The findings demonstrate the robustness of this approach across various low-light conditions, ensuring adaptability beyond specific domains such as surveillance, autonomous driving, and medical imaging.The model is evaluated on diverse low-light environments, highlighting its potential for broader real-world applicability.