Entropy-Based Non-Local Means Filter for Single-Look SAR Speckle Reduction

Remote Sensing

卷:14

Juliana Gambini¹  Debora Chan²  Alejandro C. Frery³ 

[1] Departamento de Ingeniería Informática, Instituto Tecnológico de Buenos Aires, Av. Madero 399, Buenos AiresC1106ACD, Argentina;

[2] Facultad Regional Buenos Aires, Universidad Tecnológica Nacional, Ciudad Autonoma de Buenos Aires C1179AAQ, Argentina;

[3] School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand;

关键词: non-local means;    speckle filter;    h-ϕ entropies;    asymptotic variance;   

DOI  :  10.3390/rs14030509

来源: DOAJ

【 摘 要 】

Speckle is an interference phenomenon that contaminates images captured by coherent illumination systems. Due to its multiplicative and non-Gaussian nature, it is challenging to eliminate. The non-local means approach to noise reduction has proven flexible and provided good results. We propose in this work a new non-local means filter for single-look speckled data using the Shannon and Rényi entropies under the ${\mathcal{G}}^0$ model. We obtain the necessary mathematical apparatus (the Fisher information matrix and asymptotic variance of maximum likelihood estimators). The similarity between samples of the patches relies on a parametric statistical test that verifies the evidence whether two samples have the same entropy or not. Then, we build the convolution mask by transforming the p-value into weights with a smooth activation function. The results are encouraging, as the filtered images have a better signal-to-noise ratio, they preserve the mean, and the edges are not severely blurred. The proposed algorithm is compared with three successful filters: SRAD (Speckle Reducing Anisotropic Diffusion), Lee, and FANS (Fast Adaptive Nonlocal SAR Despeckling), showing the new method’s competitiveness.

【 授权许可】

Unknown