IEEE Trans Image Process - Alternating minimization algorithm for speckle reduction with a shifting technique.

Tópicos

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Resumo

Speckles (multiplicative noise) in synthetic aperture radar (SAR) make it difficult to interpret the observed image. Due to the edge-preserving feature of total variation (TV), variational models with TV regularization have attracted much interest in reducing speckles. Algorithms based on the augmented Lagrangian function have been proposed to efficiently solve speckle-reduction variational models with TV regularization. However, these algorithms require inner iterations or inverses involving the Laplacian operator at each iteration. In this paper, we adapt Tseng's alternating minimization algorithm with a shifting technique to efficiently remove the speckle without any inner iterations or inverses involving the Laplacian operator. The proposed method is very simple and highly parallelizable; therefore, it is very efficient to despeckle huge-size SAR images. Numerical results show that our proposed method outperforms the state-of-the-art algorithms for speckle-reduction variational models with a TV regularizer in terms of central-processing-unit time.

Resumo Limpo

speckl multipl nois synthet apertur radar sar make difficult interpret observ imag due edgepreserv featur total variat tv variat model tv regular attract much interest reduc speckl algorithm base augment lagrangian function propos effici solv specklereduct variat model tv regular howev algorithm requir inner iter invers involv laplacian oper iter paper adapt tseng altern minim algorithm shift techniqu effici remov speckl without inner iter invers involv laplacian oper propos method simpl high paralleliz therefor effici despeckl huges sar imag numer result show propos method outperform stateoftheart algorithm specklereduct variat model tv regular term centralprocessingunit time

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