scholarly journals Collocation Method for Multiplicative Noise Removal Model

2020 ◽  
Vol 39 (4) ◽  
pp. 734-743
Author(s):  
Mushtaq Ahmad Khan ◽  
Zawar Hussain Khan ◽  
Haseeb khan ◽  
Sheraz Khan ◽  
Suhail Khan
Keyword(s):  
Image Denoising ◽  
Multiplicative Noise ◽  
Fundamental Problem ◽  
Lagrange Equation ◽  
Noise Removal ◽  
Original Image ◽  
General Technique ◽  
Denoising Method ◽  
Tv Regularization ◽  
Multiplicative Noise Removal

Image denoising is a fundamental problem in both image processing and computer vision with numerous applications. It can be formulated as an inverse problem. Variational methods are commonly used to solve noise removal problems. The Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with multiplicative noise into a more general technique for inverse problems such as denoising, deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. Multiplicative noise removal based on TV regularization has been widely researched in image science. In multiplicative noise problems, original image is multiplied by a noise rather than added to the original image. This article proposes a novel meshless collocation technique for the solution of a model having multiplicative noise. This technique includes TV and local collocation along with Multiquadric Radial Basis Function (MQ-RBF) for the solution of associated Euler-Lagrange equation for restoring multiplicative noise from digital images. Numerical examples demonstrate that the proposed algorithm is able to preserve small image details while the noise in the homogeneous regions is removed sufficiently. As a consequence, our method yields better denoised results than those of the current state of the art methods with respect to the Peak-Signal to Noise Ratio (PSNR) values.

scholarly journals Remote Sensing Image Denoising via Low-Rank Tensor Approximation and Robust Noise Modeling

2020 ◽  
Vol 12 (8) ◽  
pp. 1278 ◽  
Author(s):  
Tian-Hui Ma ◽  
Zongben Xu ◽  
Deyu Meng
Keyword(s):  
Remote Sensing ◽  
Image Denoising ◽  
Fundamental Problem ◽  
Remote Sensing Image ◽  
Noise Removal ◽  
Low Rank ◽  
Denoising Method ◽  
Noise Modeling ◽  
Rank Tensor ◽  
Tensor Approximation

Noise removal is a fundamental problem in remote sensing image processing. Most existing methods, however, have not yet attained sufficient robustness in practice, due to more or less neglecting the intrinsic structures of remote sensing images and/or underestimating the complexity of realistic noise. In this paper, we propose a new remote sensing image denoising method by integrating intrinsic image characterization and robust noise modeling. Specifically, we use low-Tucker-rank tensor approximation to capture the global multi-factor correlation within the underlying image, and adopt a non-identical and non-independent distributed mixture of Gaussians (non-i.i.d. MoG) assumption to encode the statistical configurations of the embedded noise. Then, we incorporate the proposed image and noise priors into a full Bayesian generative model and design an efficient variational Bayesian algorithm to infer all involved variables by closed-form equations. Moreover, adaptive strategies for the selection of hyperparameters are further developed to make our algorithm free from burdensome hyperparameter-tuning. Extensive experiments on both simulated and real multispectral/hyperspectral images demonstrate the superiority of the proposed method over the compared state-of-the-art ones.

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 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.

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