Edge-Guided Low-Light Image Enhancement Based on GAN with Effective Modules

Under low-light conditions, the images taken may not be satisfactorily bright and may degrade in appearance. Low-light image enhancement (LLIE) is a process that converts such dim images into images taken under normal lighting conditions. The primary objectives of LLIE are to diminish noise and artifacts, maintain the integrity of edges and textures, and restore the image's natural brightness and colors. Deep learning-based methods have shown remarkable success in this field recently but are hindered by their lengthy processing times due to intricate network archi-tectures. To address the balance between performance and processing speed, we introduce a streamlined network equipped with efficient modules. Our approach incorporates a GAN (Generative Adversarial Network) framework enhanced with preprocessing for edge and texture extraction. We also integrate Channel Attention for color and illumination correction, Res FFT-ReLU for noise re-duction, and Pixel Shuffler for high-frequency detail preservation. Our experiments show that our method surpasses traditional LLIE techniques in both quality and processing speed.