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
fDate :
Oct. 29 2006-Nov. 1 2006
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;
Conference_Titel :
Signals, Systems and Computers, 2006. ACSSC '06. Fortieth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
1-4244-0784-2
Electronic_ISBN :
1058-6393
DOI :
10.1109/ACSSC.2006.355096