Recent Progress in Image Deblurring

TL;DR

This review paper summarizes recent advances in image deblurring techniques, discussing their categories, challenges, and future directions to improve image restoration quality in complex environments.

Contribution

It provides a comprehensive overview and deep insights into various deblurring methods, highlighting their strengths, limitations, and potential future research directions.

Findings

Categorizes deblurring methods into five groups.

Identifies challenges in blind deblurring due to complex conditions.

Discusses promising future research directions.

Abstract

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.