  Image deblurring is a classic problem in low-level computer vision with the
aim to recover a sharp image from a blurred input image. Advances in deep
learning have led to significant progress in solving this problem, and a large
number of deblurring networks have been proposed. This paper presents a
comprehensive and timely survey of recently published deep-learning based image
deblurring approaches, aiming to serve the community as a useful literature
review. We start by discussing common causes of image blur, introduce benchmark
datasets and performance metrics, and summarize different problem formulations.
Next, we present a taxonomy of methods using convolutional neural networks
(CNN) based on architecture, loss function, and application, offering a
detailed review and comparison. In addition, we discuss some domain-specific
deblurring applications including face images, text, and stereo image pairs. We
conclude by discussing key challenges and future research directions.
