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Total generalized variation denoising of speckled images using a primal-dual algorithm

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Abstract

In this paper, multiplicative noise removal problem is considered. Multiplicative noise is also commonly referred to as speckle. We propose a total generalized variation (TGV) based model which involves the multilook M for multiplicative noise removal. The objective function of our proposed model is strictly convex. Computationally, by using the dual representation of the second-order TGV regularization, our proposed model is transformed into a minimax problem. A first-order primal-dual algorithm is developed to solve the minimax problem and the convergence of the proposed algorithm is strictly proved. At last, compared with several existing methods, experimental results demonstrate the effectiveness of the proposed method, in terms of signal-to-noise ratio, peak signal-to-noise ratio and mean absolute-deviation error.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  1. Institute of Mathematics, Hebei University of Technology, Tianjin, 300401, China

    Yehu Lv

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  1. Yehu Lv
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Lv, Y. Total generalized variation denoising of speckled images using a primal-dual algorithm. J. Appl. Math. Comput. 62, 489–509 (2020). https://doi.org/10.1007/s12190-019-01293-8

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  • DOI: https://doi.org/10.1007/s12190-019-01293-8

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