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Spatially Adapted Total Variation Model to Remove Multiplicative Noise

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2 Author(s)
Dai-Qiang Chen ; Department of Mathematics and System, School of Sciences, National University of Defense Technology, Changsha, China ; Li-Zhi Cheng

Multiplicative noise removal based on total variation (TV) regularization has been widely researched in image science. In this paper, inspired by the spatially adapted methods for denoising Gaussian noise, we develop a variational model, which combines the TV regularizer with local constraints. It is also related to a TV model with spatially adapted regularization parameters. The automated selection of the regularization parameters is based on the local statistical characteristics of some random variable. The corresponding subproblem can be efficiently solved by the augmented Lagrangian method. Numerical examples demonstrate that the proposed algorithm is able to preserve small image details, whereas the noise in the homogeneous regions is sufficiently removed. As a consequence, our method yields better denoised results than those of the current state-of-the-art methods with respect to the signal-to-noise-ratio values.

Published in:

IEEE Transactions on Image Processing  (Volume:21 ,  Issue: 4 )