Clean Image

"Clean image" research encompasses methods for improving image quality by removing various degradations, such as noise, blur, motion artifacts, and rain streaks. Current efforts focus on developing advanced deep learning models, including transformers and generative adversarial networks (GANs), often incorporating techniques like contrastive learning and Bayesian frameworks to achieve superior performance in tasks like deblurring, denoising, and inpainting. These advancements have significant implications for diverse applications, including medical imaging (improving diagnostic accuracy), industrial quality control (enhancing defect detection), and autonomous systems (improving robustness in challenging conditions).