scholarly journals Image Enhancement under Data-Dependent Multiplicative Gamma Noise

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jidesh Pacheeripadikkal ◽  
Bini Anattu
Keyword(s):  
Anisotropic Diffusion ◽  
Multiplicative Noise ◽  
State Of The Art ◽  
Noise Removal ◽  
Diffusion Term ◽  
Art Restoration ◽  
Locally Adaptive ◽  
Edge Points ◽  
Diffusive Term ◽  
Map Estimator

An edge enhancement filter is proposed for denoising and enhancing images corrupted with data-dependent noise which is observed to follow a Gamma distribution. The filter is equipped with three terms designed to perform three different tasks. The first term is an anisotropic diffusion term which is derived from a locally adaptivep-laplacian functional. The second term is an enhancement term or a shock term which imparts a shock effect at the edge points making them sharp. The third term is a reactive term which is derived based on the maximum a posteriori (MAP) estimator and this term helps the diffusive term to perform a Gamma distributive data-dependent multiplicative noise removal from images. And moreover, this reactive term ensures that deviation of the restored image from the original one is minimum. This proposed filter is compared with the state-of-the-art restoration models proposed for data-dependent multiplicative noise.

scholarly journals A hybrid regularizers model for multiplicative noise removal

2021 ◽  
pp. 40-50
Author(s):  
Thi Thu Thao Tran ◽  
Cong Thang Pham ◽  
Duc Hong Vo ◽  
Duc Hoang Vo
Keyword(s):  
Variational Method ◽  
Minimization Problem ◽  
Existence And Uniqueness ◽  
Multiplicative Noise ◽  
State Of The Art ◽  
Noise Removal ◽  
Alternating Minimization ◽  
Minimization Method ◽  
Multiplicative Noise Removal ◽  
Alternating Minimization Method

In this paper, we propose a variational method for restoring images corrupted by multiplicative noise. Computationally, we employ the alternating minimization method to solve our minimization problem. We also study the existence and uniqueness of the proposed problem. Finally, experimental results are provided to demonstrate the superiority of our proposed hybrid model and algorithm for image denoising in comparison with state-of-the-art methods.

Iteratively Reweighted Anisotropic-TV Based Multiplicative Noise Removal Model

2012 ◽  
Vol 38 (3) ◽  
pp. 444-451 ◽  
Author(s):  
Xu-Dong WANG ◽  
Xiang-Chu FENG ◽  
Lei-Gang HUO
Keyword(s):  
Multiplicative Noise ◽  
Noise Removal ◽  
Multiplicative Noise Removal ◽  
Removal Model

scholarly journals Despeckling PolSAR Images with a Structure Tensor Filter

2020 ◽  
Author(s):  
D Santana-Cedres ◽  
L Gomez ◽  
L Alvarez ◽  
Alejandro Frery
Keyword(s):  
Monte Carlo ◽  
Anisotropic Diffusion ◽  
State Of The Art ◽  
Monte Carlo Analysis ◽  
Structure Tensor ◽  
Synthetic Aperture ◽  
Experimental Setup ◽  
Polarimetric Synthetic Aperture Radar ◽  
Wishart Matrices ◽  
Dominant Direction

© 2004-2012 IEEE. In this letter, we propose a new despeckling filter for fully polarimetric synthetic aperture radar (PolSAR) images defined by 3× 3 complex Wishart distributions. We first generalize the well-known structure tensor to deal with PolSAR data which allows to efficiently measure the dominant direction and contrast of edges. The generalization includes stochastic distances defined in the space of the Wishart matrices. Then, we embed the formulation into an anisotropic diffusion-like schema to build a filter able to reduce speckle and preserve edges. We evaluate its performance through an innovative experimental setup that also includes Monte Carlo analysis. We compare the results with a state-of-the-art polarimetric filter.

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