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Deblurring using regularized locally adaptive kernel regression.

Publication ,  Journal Article
Takeda, H; Farsiu, S; Milanfar, P
Published in: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
April 2008

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.

Duke Scholars

Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

April 2008

Volume

17

Issue

4

Start / End Page

550 / 563

Related Subject Headings

  • Signal Processing, Computer-Assisted
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Regression Analysis
  • Models, Statistical
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Computer Simulation
  • Artificial Intelligence & Image Processing
  • Artifacts
 

Citation

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Takeda, H., Farsiu, S., & Milanfar, P. (2008). Deblurring using regularized locally adaptive kernel regression. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 17(4), 550–563. https://doi.org/10.1109/tip.2007.918028
Takeda, H., S. Farsiu, and P. Milanfar. “Deblurring using regularized locally adaptive kernel regression.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society 17, no. 4 (April 2008): 550–63. https://doi.org/10.1109/tip.2007.918028.
Takeda H, Farsiu S, Milanfar P. Deblurring using regularized locally adaptive kernel regression. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2008 Apr;17(4):550–63.
Takeda, H., et al. “Deblurring using regularized locally adaptive kernel regression.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, vol. 17, no. 4, Apr. 2008, pp. 550–63. Epmc, doi:10.1109/tip.2007.918028.
Takeda H, Farsiu S, Milanfar P. Deblurring using regularized locally adaptive kernel regression. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2008 Apr;17(4):550–563.

Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

April 2008

Volume

17

Issue

4

Start / End Page

550 / 563

Related Subject Headings

  • Signal Processing, Computer-Assisted
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Regression Analysis
  • Models, Statistical
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Computer Simulation
  • Artificial Intelligence & Image Processing
  • Artifacts