Publications

Abstract: Image denoising, degraded by speckle noise, has turned into a prevalent problem in image processing. Speckle noise provides a granular perspective into the image and makes it difficult to have a clear interpretation of the singular and non-singular points. This paper proposes a novel statistical approach based on Gaussian Copula modeling in the Shearlet domain. The proposed multi-dimensional Minimum Mean Square Error (MMSE) processor consists of two key components. First, Bi Parameter Cauchy Gaussian Mixture model (BCGM) as the marginal distribution of shearlet coefficients is employed. Second, the joint-prior distribution modeling is formed, based on proposing the Gaussian copula, to model the dependency of the target coefficient respect to its neighbors. The closed form mathematical expression of proposed multi dimensional MMSE processor has some computational advantages, such as parallel computing. It will be shown that the proposed processor has adaptive behavior, meaning that, working non-linearly according to the estimated noise variance on each scale and direction of the shratlet transform. This behavior confirms that the designed processor is not sensitive to initial parameter settings, unlike the state-of-the-art filters in this area.

@article{MORTEZA2022108340,
title = {A Novel Gaussian-Copula modeling for image despeckling in the shearlet domain},
journal = {Signal Processing},
volume = {192},
pages = {108340},
year = {2022},
issn = {0165-1684},
doi = {https://doi.org/10.1016/j.sigpro.2021.108340},
url = {https://www.sciencedirect.com/science/article/pii/S0165168421003777},
author = {Arian Morteza and Maryam Amirmazlaghani},
keywords = {Multiplicative noise, Non sub sampled shearlet transform, BCGM, Gaussian-Copula, Shearlet transform},
}
    

To be added.

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},
}
    

To be added.