Image Distortion

Image distortion, encompassing various degradations like noise, blur, compression artifacts, and geometric changes, significantly impacts image quality and the performance of computer vision systems. Current research focuses on developing robust image quality assessment metrics and distortion correction methods, employing architectures such as Swin Transformers, U-Nets, and generative models to address these challenges across diverse applications including medical imaging, remote sensing, and object detection. These advancements are crucial for improving the reliability and accuracy of numerous computer vision tasks and enhancing the quality of images and videos in various real-world scenarios. The development of more robust models that are less sensitive to various types of distortions is a key area of ongoing research.