Deep Image Deblurring: A Survey

By Kaihao Zhang, Wenqi Ren, Wenhan Luo, Wei-Sheng Lai, B. Stenger, Ming-Hsuan Yang, Hongdong Li. International Journal of Computer Vision, 2022

385 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/fd9c02839f326ed62d8286ba78fc7093c27655a7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

  1. Tong Lu
  2. Wenhan Luo
  3. B. Stenger
  4. Tao Wang
  5. Tianrun Shen
  6. Kaihao Zhang
  7. Luc Van Gool
  8. Andrew Zisserman

In Media Technology

  1. B. Stenger
  2. Wenhan Luo
  3. Tong Lu
  4. Kaihao Zhang

Citations in the corpus