A Review of Deep Learning-Based Image Deblurring
TL;DR
The general ideas, typical examples and applications of the four main architectural styles, as well as their particular advantages and deficiencies in different blurring conditions, are introduced.
Abstract
As an urgent problem in image restoration, deblurring aims to recover the original clear image of a blurred picture that has been damaged by motion, defocus, etc. Most of the above are explicit blur-kernel estimates. However, the distribution of these is not uniform. With the progress of deep learning in recent years, many end-to-end methods have been developed based on CNNs, RNNs, GANs and Transformers to reduce the need for hand-crafted priors. The first is to list some types of blur and their corresponding formulas. Then, it will introduce the general ideas, typical examples and applications of the four main architectural styles, as well as their particular advantages and deficiencies in different blurring conditions. List some representative benchmark datasets and image quality assessment methods from the survey, then discuss the main problems of computational efficiency, cross-domain generalization and lightweight deployment, and future research directions.