Regularized kernel regression for image deblurring
Journal articles
Takeda, H; Farsiu, S; Milanfar, P
Published in: Conference Record Asilomar Conference on Signals Systems and Computers
January 1, 2006
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.
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Conference Record Asilomar Conference on Signals Systems and Computers
DOI
ISSN
1058-6393
Publication Date
January 1, 2006
Start / End Page
1914 / 1918
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Takeda, H., Farsiu, S., & Milanfar, P. (2006). Regularized kernel regression for image deblurring. Conference Record Asilomar Conference on Signals Systems and Computers, 1914–1918. https://doi.org/10.1109/ACSSC.2006.355096
Takeda, H., S. Farsiu, and P. Milanfar. “Regularized kernel regression for image deblurring.” Conference Record Asilomar Conference on Signals Systems and Computers, January 1, 2006, 1914–18. https://doi.org/10.1109/ACSSC.2006.355096.
Takeda H, Farsiu S, Milanfar P. Regularized kernel regression for image deblurring. Conference Record Asilomar Conference on Signals Systems and Computers. 2006 Jan 1;1914–8.
Takeda, H., et al. “Regularized kernel regression for image deblurring.” Conference Record Asilomar Conference on Signals Systems and Computers, Jan. 2006, pp. 1914–18. Scopus, doi:10.1109/ACSSC.2006.355096.
Takeda H, Farsiu S, Milanfar P. Regularized kernel regression for image deblurring. Conference Record Asilomar Conference on Signals Systems and Computers. 2006 Jan 1;1914–1918.
Published In
Conference Record Asilomar Conference on Signals Systems and Computers
DOI
ISSN
1058-6393
Publication Date
January 1, 2006
Start / End Page
1914 / 1918