Blur Removal

Blur removal aims to restore sharp, clear images from blurry inputs, a crucial task with applications in various fields. Current research focuses on improving the efficiency and accuracy of existing deep learning models, exploring alternative approaches like dictionary-based methods and wavelet transforms to reduce computational costs and enhance performance. Furthermore, researchers are investigating techniques to handle diverse blur types, including out-of-focus blur and motion blur, often incorporating multi-view or multi-exposure image fusion strategies. These advancements are leading to improved image quality in diverse applications, such as photography, microscopy, and medical imaging.