Deblurring using regularized locally adaptive kernel regression.

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Takeda, H; Farsiu, S; Milanfar, P

Published Date

  • April 2008

Published In

Volume / Issue

  • 17 / 4

Start / End Page

  • 550 - 563

PubMed ID

  • 18390363

International Standard Serial Number (ISSN)

  • 1057-7149

Digital Object Identifier (DOI)

  • 10.1109/TIP.2007.918028

Language

  • eng

Conference Location

  • United States