Defocus Deblurring

Defocus deblurring aims to computationally remove blur from images caused by a shallow depth of field, a common problem in photography and microscopy. Current research focuses on developing deep learning models, often employing transformer architectures or diffusion models, to achieve high-quality deblurring, particularly addressing challenges like misaligned training data and limited datasets through techniques such as contrastive learning and reblurring. These advancements are significant for improving image quality in various applications, ranging from medical imaging and microscopy to enhancing smartphone photography and enabling creative post-processing effects. The development of efficient models that minimize computational cost is also a key area of ongoing investigation.