Artistic Image Enhancement Based on Iterative Contrastive Learning

This research work presents a novel artistic image enhancement algorithm based on iterative contrastive learning to address the challenges of image degradation in computer vision systems. The proposed methodology integrates efficient de-noising, oversampling weight incorporation, and probability distribution control for comprehensive image enhancement. The algorithm is trained on a diverse data-set of low-light images and corresponding references, demonstrating its adaptability to different scenarios. Simulation results demonstrate the effectiveness of the algorithm in improving image quality, highlighting its potential for advancements in computational visual systems. The proposed approach contributes to ongoing research in image processing and enhancement, and offers a promising solution to the challenges posed by diverse operational environments and degradation issues in optical representations