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Edge-Aware Unified Retinex Architecture for Adaptive Low-light Enhancement of Images and Videos Across Diverse Domains in Real-time

Author(s) Ms. C Amrutha, E. Sudheer, K. Sunil Kumar Reddy, K. Sowjanya, Chatta Balaji
Country India
Abstract : Low-light picture and video degradation is still a significant problem in fields including smartphone photography, autonomous navigation, surveillance, and medical imaging. In order to get visually consistent outcomes in extremely low illumination, this study presents a real-time improvement system that combines deep learning with traditional image-processing approaches. The framework uses CLAHE for adaptive contrast enhancement, Non-Local Means for noise suppression, and Zero-DCE++ for deep learning-based light correction. Effective preparation and seamless real-time deployment are ensured by additional elements including bilinear scaling, color-space conversions, and OpenCV-based video input/output operations. The system is feasible for mobile and embedded devices since it is built with lightweight modules that support GPU acceleration, allowing processing speeds exceeding 30 FPS. When compared to traditional low-light enhancement methods, experimental evaluation on several benchmark datasets shows that the suggested method greatly enhances brightness, contrast, noise reduction, color accuracy, PSNR, SSIM, and overall perceptual quality. The outcomes demonstrate how well deep learning and tailored image-processing techniques work together to provide reliable, real-time low-light augmentation.
Keywords Low light enhancement, Zero-DCE++, Deep Learning, OpenCV.
Field Engineering
Published In Volume 7, Issue 4, April 2026
Published On 2026-04-04
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