An Improved Image Denoising Method Based on Multi-scale Correlation in Wavelet Domain

2006 ◽  
Author(s):  
Guanghong He ◽  
Yingjun Pan ◽  
Wei Jin
Keyword(s):  
Image Denoising ◽  
Wavelet Domain ◽  
Denoising Method ◽  
Multi Scale

Image denoising method used in earthquake ruin scene based on multi-scale mathematical morphology

2012 ◽  
Vol 10 (s2) ◽  
pp. S21002-321004 ◽  
Author(s):  
Yanxing Song Yanxing Song ◽  
Shucong Liu Shucong Liu ◽  
Jingsong Yang Jingsong Yang
Keyword(s):  
Image Denoising ◽  
Mathematical Morphology ◽  
Denoising Method ◽  
Multi Scale

scholarly journals Image Denoising Using Singular Value Difference in the Wavelet Domain

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Min Wang ◽  
Wei Yan ◽  
Shudao Zhou
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Values ◽  
Singular Value ◽  
Wavelet Domain ◽  
Noisy Image ◽  
Denoising Method ◽  
Diagonal Part

Singular value (SV) difference is the difference in the singular values between a noisy image and the original image; it varies regularly with noise intensity. This paper proposes an image denoising method using the singular value difference in the wavelet domain. First, the SV difference model is generated for different noise variances in the three directions of the wavelet transform and the noise variance of a new image is used to make the calculation by the diagonal part. Next, the single-level discrete 2-D wavelet transform is used to decompose each noisy image into its low-frequency and high-frequency parts. Then, singular value decomposition (SVD) is used to obtain the SVs of the three high-frequency parts. Finally, the three denoised high-frequency parts are reconstructed by SVD from the SV difference, and the final denoised image is obtained using the inverse wavelet transform. Experiments show the effectiveness of this method compared with relevant existing methods.

Bayesian Image Denoising Using an Anisotropic Markov Random Field Model

2011 ◽  
Vol 467-469 ◽  
pp. 2018-2023
Author(s):  
Yan Qiu Cui ◽  
Tao Zhang ◽  
Shuang Xu ◽  
Hou Jie Li
Keyword(s):  
Computational Complexity ◽  
Random Field ◽  
Image Denoising ◽  
Markov Random Field ◽  
Field Model ◽  
Wavelet Domain ◽  
Map Estimation ◽  
Denoising Method ◽  
Markov Random ◽  
Restoration Method

This paper presents a Bayesian denoising method based on an anisotropic Markov Random Field (MRF) model in wavelet domain in order to improve the image denoising performance and reduce the computational complexity. The classical single-resolution image restoration method using MRFs and the maximum a posteriori (MAP) estimation is extended to the wavelet domain. To obtain the accurate MAP estimation, a novel anisotropic MRF model is proposed under this framework. As compared to the simple isotropic MRF model, this new model can capture the intrascale dependencies of wavelet coefficients significantly better. Simulation results demonstrate our proposed method has a good denoising performance while reducing the computational complexity.

scholarly journals A New Image Denoising Method with Gan Models

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Xueji Huang
Keyword(s):  
Image Denoising ◽  
Image Features ◽  
Denoising Method ◽  
Residual Structure ◽  
Multi Scale ◽  
Residual Noise ◽  
Theoretical System ◽  
Feature Filtering ◽  
Low Dimensional ◽  
Denoised Image

In order to obtain clear images and solve the problems of low image quality caused by noise disturbance, a lot of researches have been done on image denoising techniques. In the theoretical system of algorithms studied so far, many algorithms can effectively remove noise in low-dimensional images, but at the same time, the results are slightly inferior when processing high-dimensional images. This paper proposes a q-GAN, which uses multi-scale in generating networks. The convolution kernel extracts image features and transforms the denoising problem into the feature domain. In the feature domain, a residual structure is used to denoise, and the noise distribution is removed from the feature distribution. There are residual noise features in the obtained denoising features, which are removed by subsequent feature filtering of the network structure, and finally a denoised image is generated by fusing the noiseless features.

Denoising Method for Unknown Image Noise Based on FWT Optimal Order Selection

2013 ◽  
Vol 765-767 ◽  
pp. 2776-2780
Author(s):  
Yuan Yuan Jiang ◽  
You Ren Wang ◽  
Hui Luo
Keyword(s):  
Image Denoising ◽  
Fractional Order ◽  
Objective Evaluation ◽  
Input Image ◽  
Image Noise ◽  
Optimal Order ◽  
Wavelet Domain ◽  
Denoising Method ◽  
Evaluation Standard ◽  
Actual Application

The optimal fractional order is got for image denoising by 2-D fractional wavelet transform (FWT). But, the actual application environment is complex, and the input image has already been polluted by unknown noise frequently in the process of capture and transmission. And it's impossible to get the optimal fractional order on the basis of the objective evaluation standard in existence. Therefore, in view of the unknown image noise, a method to get the estimated value of optimal fractional order is put forward. Firstly, new objective evaluation standards for image denoising in fractional wavelet domain are defined, and its optimal value is obtained based on noise estimation. Then the optimal estimated fractional order is got. The experiment results show that, the optimal order of 2-D FWT can be selected reasonably by the proposed method and the unknown image noise can be filtered effectively in the estimated optimal fractional wavelet domain.

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
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