Focus Blur

Focus blur, encompassing both out-of-focus and motion blur, presents a significant challenge in image processing, with research primarily focused on developing effective deblurring techniques. Current approaches leverage deep learning architectures, such as U-Nets and YOLO variations, alongside novel loss functions and dictionary-based methods to improve image sharpness and enhance object detection accuracy, particularly in challenging scenarios like aerial imagery. These advancements have implications for various applications, including improving the quality of photographs, enhancing medical imaging, and enabling more robust object detection in autonomous systems.