A non-linear image denoising method for salt-&-pepper noise removal using Fuzzy-Based approach

2011 ◽  
Author(s):  
S. Muthukumar ◽  
G. Raju
Keyword(s):  
Image Denoising ◽  
Noise Removal ◽  
Denoising Method ◽  
Non Linear

scholarly journals Face Image Denoising Method Based on Fourth-Order Partial Differential Equations

2012 ◽  
Vol 6-7 ◽  
pp. 700-703
Author(s):  
Weng Cang Zhao ◽  
Fan Wang
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Image Denoising ◽  
Fourth Order ◽  
Face Image ◽  
Partial Differential ◽  
Denoising Method ◽  
Linear Diffusion ◽  
Non Linear ◽  
Face Image Processing

In order to improve the effect of face image denoising, this paper put forward several face image denoising methods based on partial differential equations, including P-M non-linear diffusion equations and fourth-order partial differential equations. We use those two methods by establishing non-linear diffusion equations and fourth-order anisotropic diffusion partial differential equation. The P-M non-linear diffusion denoising method can remove noise in intra-regions sufficiently but noise at edges can not be eliminated successfully and line-like structures can not be held very well.While the fourth-order partial differential equations denoising can retain the local detail characteristics of the original face image. Finally, through the experimental results we can see the effect of the fourth-order partial differential equations denoising is better, which makes the later face image processing more accurate and promotes the development of face image processing.

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.

A Novel Weighted Variational Model for Image Denoising

2017 ◽  
Vol 31 (12) ◽  
pp. 1754022 ◽  
Author(s):  
Md. Robiul Islam ◽  
Chen Xu ◽  
Yu Han ◽  
Rana Aamir Raza Ashfaq
Keyword(s):  
Image Analysis ◽  
Image Denoising ◽  
Noise Removal ◽  
Variational Model ◽  
Denoising Method ◽  
Structural Details ◽  
Classical Models ◽  
Dual Projection ◽  
Image Detail ◽  
Novel Algorithm

Image denoising as a part of pre-processing in image analysis is a challenging area of research since noise removal and image detail preservation need a tradeoff. For classical denoising models, the convex total variation (TV) or some nonconvex regularizers are used to achieve the tradeoff. However, the denoising performance of classical models is still inadequate. To overcome this problem, this paper proposes a new variational model for image restoration, where a weighted regularizer is designed to protect more geometric structural details of images from over-smoothing and to remove much noise simultaneously. To solve the model efficiently, a novel algorithm based on Chambolle’s dual projection method and the iteratively reweighting method is presented. Numerical results prove that the proposed denoising method can show better performance than the classical TV-based and the nonconvex regularizer-based denoising methods.

scholarly journals A Novel Directionlet-Based Image Denoising Method Using MMSE Estimator and Laplacian Mixture Distribution

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yixiang Lu ◽  
Qingwei Gao ◽  
Dong Sun ◽  
Dexiang Zhang ◽  
Yi Xia ◽  
...  
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Minimum Mean Square Error ◽  
Expectation Maximization Algorithm ◽  
Mixture Distribution ◽  
Weighted Average ◽  
Noise Removal ◽  
Error Estimator ◽  
Denoising Method

A novel method based on directionlet transform is proposed for image denoising under Bayesian framework. In order to achieve noise removal, the directionlet coefficients of the uncorrupted image are modeled independently and identically by a two-state Laplacian mixture model with zero mean. The expectation-maximization algorithm is used to estimate the parameters that characterize the assumed prior model. Within the framework of Bayesian theory, the directionlet coefficients of noise-free image are estimated by a nonlinear shrinkage function based on weighted average of the minimum mean square error estimator. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the proposed method is very competitive when compared with other methods in terms of both peak signal-to-noise ratio and visual quality.

An image Denoising Method Based On Multi Resulation Bilateral Filter

2018 ◽  
Vol 6 (12) ◽  
pp. 448-452
Author(s):  
Md Shaiful Islam Babu ◽  
Kh Shaikh Ahmed ◽  
Md Samrat Ali Abu Kawser ◽  
Ajkia Zaman Juthi
Keyword(s):  
Image Denoising ◽  
Bilateral Filter ◽  
Denoising Method

Sonar image denoising method based on NSCT circle sample

2009 ◽  
Vol 29 (1) ◽  
pp. 68-70
Author(s):  
Chun-rui TANG ◽  
Dan-dan LIU
Keyword(s):  
Image Denoising ◽  
Denoising Method ◽  
Sonar Image

Improved anisotropic diffusion ultrasound image denoising method based on logarithmic compression

2013 ◽  
Vol 32 (11) ◽  
pp. 3218-3220
Author(s):  
Jin YANG ◽  
Zhi-qin LIU ◽  
Yao-bin WANG ◽  
Xiao-ming GAO
Keyword(s):  
Image Denoising ◽  
Anisotropic Diffusion ◽  
Ultrasound Image ◽  
Denoising Method

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

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