Sciweavers

Share
TIP
2008

Deblurring Using Regularized Locally Adaptive Kernel Regression

9 years 10 months ago
Deblurring Using Regularized Locally Adaptive Kernel Regression
Kernel regression is an effective tool for a variety of image processing tasks such as denoising and interpolation [1]. In this paper, we extend the use of kernel regression for deblurring applications. In some earlier examples in the literature, such nonparametric deblurring was suboptimally performed in two sequential steps, namely denoising followed by deblurring. In contrast, our optimal solution jointly denoises and deblurs images. The proposed algorithm takes advantage of an effective and novel image prior that generalizes some of the most popular regularization techniques in the literature. Experimental results demonstrate the effectiveness of our method.
Hiroyuki Takeda, Sina Farsiu, Peyman Milanfar
Added 28 Jan 2011
Updated 28 Jan 2011
Type Journal
Year 2008
Where TIP
Authors Hiroyuki Takeda, Sina Farsiu, Peyman Milanfar
Comments (0)
books