A novel statistical approach for multiplicative speckle removal using t-locations scale and non-sub sampled shearlet transform
Published in Digital Signal Processing, 2020
[J1] A. Morteza and M. Amirmazlaghani, "A novel statistical approach for multiplicative speckle removal using t-locations scale and non-sub sampled shearlet transform," Digital Signal Processing , vol. 107, pp. 102857, 2020.
Abstract: One of the most interesting problems in denoising of images includes despeckling of multiplicative noise. This paper proposes a novel statistical processor in the framework of Non-sub Sampled Shearlet Transform (NSST) to reduce the effect of the multiplicative noise on images given preserving of structural and visual quality of image. First, we indicate that NSST coefficients of logarithmically transformed images can be statistically meaningful modeled by t-location scale (TLS). For designing our processor, we employ Minimum Mean Squared Error (MMSE) estimator to reduce noise distortion. We show by using TLS as the prior distribution, non-linear noise suppression behavior is obtained in test images. Finally, we compare our method by the state-of-the-art algorithms like soft and hard thresholding and also with well-known adaptive filters in this area like Wiener, Frost, Lee and one recent method in shearlet denoising framework.
@article{MORTEZA2020102857,
title = {A novel statistical approach for multiplicative speckle removal using t-locations scale and non-sub sampled shearlet transform},
journal = {Digital Signal Processing},
volume = {107},
pages = {102857},
year = {2020},
issn = {1051-2004},
doi = {https://doi.org/10.1016/j.dsp.2020.102857},
url = {https://www.sciencedirect.com/science/article/pii/S1051200420302025},
author = {Arian Morteza and Maryam Amirmazlaghani},
keywords = {Despeckling, Multiplicative noise, Non-sub sampled shearlet transform, T-location scale},
}
