Decision-based fuzzy image restoration for noise reduction based on evidence theory

2011 ◽  
Vol 38 (7) ◽  
pp. 8303-8310 ◽  
Author(s):  
Tzu-Chao Lin
Keyword(s):  
Image Restoration ◽  
Noise Reduction ◽  
Evidence Theory ◽  
Fuzzy Image

scholarly journals Noise reduction by fuzzy image filtering

2003 ◽  
Vol 11 (4) ◽  
pp. 429-436 ◽  
Author(s):  
D. Van De Ville ◽  
M. Nachtegael ◽  
D. Van der Weken ◽  
E.E. Kerre ◽  
W. Philips ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Image Filtering ◽  
Fuzzy Image

Example-Based Face-Image Restoration for Block-Noise Reduction

2019 ◽  
Vol 7 (1) ◽  
pp. 9-17
Author(s):  
Suhail Hamdan ◽  
◽  
Yohei Fukumizu ◽  
Tomonori Izumi ◽  
Hironori Yamauchi
Keyword(s):  
Image Restoration ◽  
Noise Reduction ◽  
Face Image

A Research on the Optimization of Fuzzy Image

2013 ◽  
Vol 409-410 ◽  
pp. 1593-1596
Author(s):  
Xue Feng Wu ◽  
Yu Fan
Keyword(s):  
Mathematical Model ◽  
Image Restoration ◽  
Point Spread Function ◽  
Motion Blur ◽  
Inverse Filtering ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Blurred Images ◽  
Fuzzy Image

The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given According to the characteristics of blurred images the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration, Lucy-Richardson image restoration and Wiener image restoration. The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved, and the image restoration is more stable.

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