Computer Science ›› 2021, Vol. 48 ›› Issue (3): 9-13.doi: 10.11896/jsjkx.201200043

Special Issue: Advances on Multimedia Technology

• Advances on Multimedia Technology • Previous Articles     Next Articles

Research Progress on Deep Learning-based Image Deblurring

PAN Jin-shan   

  1. School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
  • Received:2020-12-03 Revised:2021-02-01 Online:2021-03-15 Published:2021-03-05
  • About author:PAN Jin-shan,born in 1985,Ph.D,professor.His main research interests include image deblurring,image/video analysis and enhancement,and related vision problems.
  • Supported by:
    National Natural Science Foundation of China(61872421) and Natural Science Foundation of Jiangsu Province (BK20180471).

Abstract: With the increasing development of portable and smart digital imaging devices,the way to capture photos is more convenient and flexible.Digital images play an important role in video surveillance,medical diagnosis,space exploration,and so on.However,the captured images usually contain significant blur and noise due to the limited quality of the camera sensors,the skill of the photographers,and the imaging environments.How to restore the clear images from blurry ones so that they can facilitate the following intelligent analysis tasks is important but challenging.Image deburring is a classical ill-posed problem.Represented methods for this problem include the statistical prior-based methods and data-driven methods.However,conventional statistical prior-based methods have limited ability for modeling the inherent properties of the clear images.The data-driven methods,especially the deep learning methods,provide an effective way to solve image deblurring.This paper focuses on the deep learning-based image deblurring methods.It first introduces the research progress of the image deblurring problem,and then analyzes the challenges of the image deblurring problem.Finally,it discusses the research trends of the image deblurring problem.

Key words: Deep learning, Ill-posed problem, Image deblurring, Motion blur, Prior modeling

CLC Number: 

  • TP391
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