An Efficient Two-Phase <formula formulatype="inline"> <tex Notation="TeX">${\rm L}^{1}$</tex></formula>-TV Method for Restoring Blurred Images with Impulse Noise

Abstract

A two-phase image restoration method based upon total variation regularization combined with an L<sup>1</sup>-data-fitting term for impulse noise removal and deblurring is proposed. In the first phase, suitable noise detectors are used for identifying image pixels contaminated by noise. Then, in the second phase, based upon the information on the location of noise-free pixels, images are deblurred and denoised simultaneously. For efficiency reasons, in the second phase a superlinearly convergent algorithm based upon Fenchel-duality and inexact semismooth Newton techniques is utilized for solving the associated variational problem. Numerical results prove the new method to be a significantly advance over several state-of-the-art techniques with respect to restoration capability and computational efficiency.

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Cite this paper

@article{Chan2010AnET, title={An Efficient Two-Phase \$\{\rm L\}^\{1\}\$-TV Method for Restoring Blurred Images with Impulse Noise}, author={Raymond H. Chan and Yiqiu Dong and Michael Hintermux0308ller}, journal={IEEE Transactions on Image Processing}, year={2010}, volume={19}, pages={1731-1739} }