DocumentCode
2461369
Title
Regularized Kernel Regression for Image Deblurring
Author
Takeda, Hiroyuki ; Farsiu, Sina ; Milanfar, Peyman
Author_Institution
Dept. of Electr. Eng., Univ. of California at Santa Cruz, Santa Cruz, CA
fYear
2006
fDate
Oct. 29 2006-Nov. 1 2006
Firstpage
1914
Lastpage
1918
Abstract
The framework of kernel regression [1], a non- parametric estimation method, has been widely used in different guises for solving a variety of image processing problems including denoising and interpolation [2]. In this paper, we extend the use of kernel regression for deblurring applications. Furthermore, we show that many of the popular image reconstruction techniques are special cases of the proposed framework. Simulation results confirm the effectiveness of our proposed methods.
Keywords
image denoising; image restoration; regression analysis; image deblurring; image denoising; image processing problems; image reconstruction techniques; regularized kernel regression; Data models; Image processing; Image reconstruction; Image restoration; Interpolation; Kernel; Noise reduction; Optical noise; TV; Video compression;
fLanguage
English
Publisher
ieee
Conference_Titel
Signals, Systems and Computers, 2006. ACSSC '06. Fortieth Asilomar Conference on
Conference_Location
Pacific Grove, CA
ISSN
1058-6393
Print_ISBN
1-4244-0784-2
Electronic_ISBN
1058-6393
Type
conf
DOI
10.1109/ACSSC.2006.355096
Filename
4176906
Link To Document