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Blind motion deblurring with cycle generative adversarial networks.
- Source :
-
Visual Computer . Aug2020, Vol. 36 Issue 8, p1591-1601. 11p. - Publication Year :
- 2020
-
Abstract
- Blind motion deblurring is one of the most basic and challenging problems in image processing and computer vision. It aims to recover a sharp image from its blurred version knowing nothing about the blurring process. Many existing methods use the maximum a posteriori or expectation maximization framework to tackle this problem, but they cannot handle well the natural images with high-frequency features. Most recently, deep neural networks have been emerging as a powerful tool for image deblurring. In this paper, we show that encoder–decoder architecture gives better results for image deblurring tasks. In addition, we propose a novel end-to-end learning model that refines the generative adversarial network by many novel strategies to tackle the problem of image deblurring. Experimental results show that our model can capture high-frequency features well, and achieve the competitive performance. [ABSTRACT FROM AUTHOR]
- Subjects :
- *COMPUTER vision
*IMAGE processing
*MOTION
*NATURAL heat convection
Subjects
Details
- Language :
- English
- ISSN :
- 01782789
- Volume :
- 36
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- Visual Computer
- Publication Type :
- Academic Journal
- Accession number :
- 144643200
- Full Text :
- https://doi.org/10.1007/s00371-019-01762-y