非盲图像复原综述

杨航

doi:10.37188/CO.2022-0099

Volume 15 Issue 5

Sep. 2022

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Chinese Optics > 2022 > 15(5): 954-972.

YANG Hang. Survey of non-blind image restoration[J]. Chinese Optics, 2022, 15(5): 954-972. doi: 10.37188/CO.2022-0099

Citation:

YANG Hang. Survey of non-blind image restoration[J]. Chinese Optics, 2022, 15(5): 954-972. doi: 10.37188/CO.2022-0099

YANG Hang. Survey of non-blind image restoration[J]. Chinese Optics, 2022, 15(5): 954-972. doi: 10.37188/CO.2022-0099

Citation:

YANG Hang. Survey of non-blind image restoration[J]. Chinese Optics, 2022, 15(5): 954-972. doi: 10.37188/CO.2022-0099

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Survey of non-blind image restoration

doi: 10.37188/CO.2022-0099

YANG Hang^,

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

Funds: Supported by Youth Innovation Promotion Association, CAS (No. 2020220)

More Information

Corresponding author: yanghang@ciomp.ac.cn
Received Date: 16 May 2022
Rev Recd Date: 20 Jun 2022

Available Online: 26 Jul 2022

Abstract

Abstract

Non-blind image restoration is one of the most improtant research topics in the field of computer vision. It is also a typical ill-posed problem in mathematics. Its goal is to estimate a clear image from a blurred image when the point spread function is known. Its research focuses on how to make an appropriate compromise between improving clarity and suppressing noise. In the past 50 years, non-blind image restoration has made great progress. From the Wiener filtering to deep learning based methods, scholars have proposed hundreds of non-blind image restoration algorithms and applied them in various academic fields. This paper first introduces the basic concept and research significance of non-blind image restoration, then classifies and summarizes the main non-blind image restoration algorithms according to the algorithm attributes, which are generally divided into traditional methods and deep learning based methods. The traditional methods are divided into the direct method and iterative method, then are analyzed for their advantages and disadvantages. The performance of representative restoration algorithms is compared in a varity of typical experiments. Finally, the development trend and important research directions of non-blind image restoration algorithms are proposed.
- non blind image restoration,
- image priors,
- direct method,
- iterative scheme,
- deep learning

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References(123)

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