Deep Image Deblurring: A Survey

TL;DR

This survey comprehensively reviews recent deep learning methods for image deblurring, covering architectures, datasets, applications, and future challenges in the field.

Contribution

It provides a detailed taxonomy and comparison of CNN-based deblurring approaches, serving as a valuable resource for researchers.

Findings

Summarizes common causes of image blur.

Introduces benchmark datasets and metrics.

Discusses domain-specific applications.

Abstract

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.