计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (3): 552-564.DOI: 10.3778/j.issn.1673-9418.2106100

• 综述·探索 • 上一篇    下一篇

单图像盲去模糊方法概述

刘利平, 孙建+(), 高世妍   

  1. 华北理工大学 人工智能学院,河北 唐山 063210
  • 收稿日期:2021-06-28 修回日期:2021-08-26 出版日期:2022-03-01 发布日期:2021-09-01
  • 通讯作者: + E-mail: 1125439094@qq.com
  • 作者简介:刘利平(1977—),女,河北唐山人,博士在读,教授,主要研究方向为模式识别与智能系统、矿业工程等。
    孙建(1997—),男,河北衡水人,硕士研究生,主要研究方向为图像处理、机器视觉、模式识别。
    高世妍(1997—),女,河北唐山人,硕士研究生,主要研究方向为图像处理、机器视觉、模式识别。
  • 基金资助:
    河北省省级科技计划(20327218D);华北理工大学研究生创新项目(2019B28);河北省省属高校基本科研业务费研究项目(JYG2019004)

Overview of Blind Deblurring Methods for Single Image

LIU Liping, SUN Jian+(), GAO Shiyan   

  1. School of Artificial Intelligence, North China University of Science and Technology, Tangshan, Hebei 063210, China
  • Received:2021-06-28 Revised:2021-08-26 Online:2022-03-01 Published:2021-09-01
  • About author:LIU Liping, born in 1977, Ph.D., professor. Her research interests include pattern recognition and intelligent system, mining engineering, etc.
    SUN Jian, born in 1997, M.S. candidate. His research interests include image processing, machine vision and pattern recognition.
    GAO Shiyan, born in 1997, M.S. candidate. Her research interests include image processing, machine vision and pattern recognition.
  • Supported by:
    Science and Technology Program of Hebei Province(20327218D);Graduate Student Innovation Fund of North China University of Science and Technology(2019B28);Fundamental Research Funds for the Provincial Universities of Hebei(JYG2019004)

摘要:

图像去模糊长期以来一直是计算机视觉和图像处理中的研究热点。由相机抖动、物体运动或失焦引起的运动模糊或焦点模糊图像会严重影响图像的使用和后续处理。传统的盲去模糊方法利用图像运动模糊产生的不同原因,可将运动模糊分为全局运动模糊和局部运动模糊。概述了近年来图像盲去模糊的方法和研究现状。在深度学习图像去模糊方法的基础上,总结了图像去模糊的方法及研究现状。同时,对传统的盲去模糊方法和深度学习的盲去模糊方法进行了分类总结,归纳了图像去模糊前所需数据集的三种构成方式和图像去模糊后的质量评价标准,然后在公共去模糊数据集上对部分传统去模糊和深度学习去模糊的方法进行了定量和定性的分析比较。最后,分析了当前图像去模糊方法面临的问题,展望了图像去模糊方法的研究趋势,并且对目前单图像盲去模糊存在的主要问题进行了拓展分析,逐一细化地说明了可用的解决方法或解决思路,为后续的研究提供了一定的理论依据。

关键词: 盲去模糊, 运动模糊, 深度学习

Abstract:

Image deblurring has been a research hotspot in computer vision and image processing for a long time. The motion blur or focus blur image caused by camera jitter, object motion or defocus will seriously affect the use and follow-up processing of the image. The traditional blind deblurring methods make use of different causes of image motion blur, dividing motion blur into global motion blur and local motion blur. This paper summarizes the methods and research status of image blind deblurring in recent years. Then, on the basis of deep learning image deblurring methods, the image deblurring methods and research status are summarized. At the same time, the traditional blind deblurring methods and deep learning blind deblurring methods are classified and summarized, and the three forms of datasets needed before image deblurring and the quality evaluation criteria after image deblurring are summarized. Then, some of the traditional deblurring and deep learning deblurring methods are quantitatively and qualitatively analyzed and compared on the public deblurring dataset. Finally, the problems faced by the current image deblurring methods are analyzed, the research trend of image deblurring methods is prospected, and the main problems existing in single image blind deblurring are analyzed. The available solutions or ideas are explained one by one, which provides a theoretical basis for the follow-up research.

Key words: blind deblurring, motion blur, deep learning

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