Abstract
Contemporary algorithms for enhancing images in low-light conditions prioritize improving brightness and contrast but often neglect improving image details. This study introduces the Swin Transformer-based Light-enhancing Generative Adversarial Network (SwinLightGAN), a novel generative adversarial network (GAN) that effectively enhances image details under low-light conditions. The network integrates a generator model based on a Residual Jumping U-shaped Network (U-Net) architecture for precise local detail extraction with an illumination network enhanced by Shifted Window Transformer (Swin Transformer) technology that captures multi-scale spatial features and global contexts. This combination produces high-quality images that resemble those taken in normal lighting conditions, retaining intricate details. Through adversarial training that employs discriminators operating at multiple scales and a blend of loss functions, SwinLightGAN ensures a seamless distinction between generated and authentic images, ensuring superior enhancement quality. Extensive experimental analysis on multiple unpaired datasets demonstrates SwinLightGAN's outstanding performance. The system achieves Naturalness Image Quality Evaluator (NIQE) scores ranging from 5.193 to 5.397, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) scores from 28.879 to 32.040, and Patch-based Image Quality Evaluator (PIQE) scores from 38.280 to 44.479, highlighting its efficacy in delivering high-quality enhancements across diverse metrics.