MULTI-SCALE RETINEX-UNET FOR ENHANCEMENT OF LOW LIGHT WEAK CONTRAST IMAGES

The enhanced quality of images is crucial in the realm of image processing applications. However, images captured in low-light environments often suffer from poor contrast and noise, leading to a loss of detailed information. To address this challenge, we propose a Multi-Scale RETINEX-UNET (MSR-UNET) model for low-light weak contrast (LLWC) image enhancement. This novel approach integrates a modified U-Net architecture with an improved multi-scale Retinex (IMSR) model, aiming to preserve natural colors while enhancing visual quality. The proposed model is validated using the Renoir dataset, and its effectiveness is measured through PSNR (34.3 dB) and SSIM (99%). Compared to conventional enhancement techniques, our model outperforms existing methods by effectively reducing noise while maintaining structural details. This study advances deep learning-based image enhancement and sets a new benchmark for LLWC image processing.