scholarly journals Blocking artefacts reduction based on a ripple matrix permutation image of high‐frequency images in the wavelet domain

2021 ◽  
Author(s):  
Xiuli Du ◽  
Jinting Liu ◽  
Wei Zhang ◽  
Ya'na Lv
Keyword(s):  
High Frequency ◽  
Wavelet Domain ◽  
Matrix Permutation

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.

scholarly journals Automatic seizure detection in SEEG using high frequency activities in wavelet domain

2013 ◽  
Vol 35 (3) ◽  
pp. 319-328 ◽  
Author(s):  
L. Ayoubian ◽  
H. Lacoma ◽  
J. Gotman
Keyword(s):  
High Frequency ◽  
Seizure Detection ◽  
Wavelet Domain

scholarly journals Analysis of the Wavelet Domain Filtering Approach for Video Super-Resolution

2021 ◽  
Vol 11 (4) ◽  
pp. 7477-7482
Author(s):  
M. V. Daithankar ◽  
S. D. Ruikar
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Super Resolution ◽  
Visual Quality ◽  
Research Area ◽  
Wavelet Domain ◽  
Edge Information ◽  
Algorithm Efficiency ◽  
Frequency Components ◽  
Processing Techniques

The wavelet domain-centered algorithms for the super-resolution research area give better visual quality and have been explored by different researchers. The visual quality is achieved with increased complexity and cost as most of the systems embed different pre- and post-processing techniques. The frequency and spatial domain-based methods are the usual approaches for super-resolution with some benefits and limitations. Considering the benefits of wavelet domain processing, this paper deals with a new algorithm that depends on wavelet residues. The methodology opts for wavelet domain filtering and residue extraction to get super-resolved frames for better visuals without embedding other techniques. The avoidance of noisy high-frequency components from low-quality videos and the consideration of edge information in the frames are the main targets of the super-resolution process. This inverse process is carried with a proper combination of information present in low-frequency bands and residual information in the high-frequency components. The efficient known algorithms always have to sacrifice simplicity to achieve accuracy, but in the proposed algorithm efficiency is achieved with simplicity. The robustness of the algorithm is tested by analyzing different wavelet functions and at different noise levels. The proposed algorithm performs well in comparison to other techniques from the same domain.

Research of Image Compression Based on Fractal Coding

2012 ◽  
Vol 241-244 ◽  
pp. 418-422
Author(s):  
Dong Mei Wang ◽  
Jing Yi Lu
Keyword(s):  
Wavelet Transform ◽  
Theoretical Analysis ◽  
Image Compression ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Simulation Experiment ◽  
Low Frequency ◽  
Reconstructed Image ◽  
Wavelet Domain ◽  
Fractal Coding

The EZW and Fractal Coding were researched and simulated in this paper. And two drawbacks were discovered in these algorithm:the coding time is too long and the effect of reconstructed image is not ideal. Therefore, The paper studied the wavelet transformation in the fractal coding application, The wavelet coefficients of an image present two characteristics when the image is processed by wavelet transform: first characteristic is that the energy of an image is strongly concentrated in low frequency sub-image, second characteristic is that there is a similarity between the same direction in high frequency sub-images.but the fractal coding essence was precisely uses the similarity of wavelet transform image. The paper designed one kind of new Image Compression based on Fractal Coding in wavelet domain. The theoretical analysis and the simulation experiment indicated that, to some extent the method can reduce the coding time and reduce the MSE and enhance compression ratio of the reconstructed image and improve PSNR of the reconstructed image..

Image Texture Balancing in the Wavelet Domain Based on a Ripple Matrix Permutation

2021 ◽  
pp. 513-520
Author(s):  
Xiuli Du ◽  
Jinting Liu ◽  
Wei Zhang ◽  
Ya’na Lv
Keyword(s):  
Image Texture ◽  
Wavelet Domain ◽  
Matrix Permutation

A Steganographic Method Based on Double Layers Wet Paper Code in IWT Domain

2012 ◽  
Vol 239-240 ◽  
pp. 1225-1231
Author(s):  
Min Qing Zhang ◽  
Xiao Ling Yang ◽  
Gang Wei Su
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Double Layers ◽  
Wavelet Domain ◽  
Integer Wavelet

Based on wet paper code, integer wavelet and matrix coding, this paper constructs a new steganographic algorithm. Combine with the advantage in integer wavelet domain, which more close to HVS, a part of secret messages are embedded by LSB in high-frequency coefficients after integer wavelet switch, wet paper code is used to change the direction of embedding. Another part of secret messages are embedded by matrix coding in low-frequency coefficients after integer wavelet switch, wet paper code is still used to change the direction of embedding. The performance of the proposed approach shows that security and capacity of secret messages are improved in integer wavelet domain.

A New Watermarking Algorithm for Relational Database Copyright Protection

2013 ◽  
Vol 385-386 ◽  
pp. 1713-1717
Author(s):  
Zhi Ping Li ◽  
Chuan Xian Jiang ◽  
Zhi Li
Keyword(s):  
Relational Database ◽  
Copyright Protection ◽  
High Frequency ◽  
Wavelet Domain ◽  
Two Dimension ◽  
Digital Watermarks ◽  
Definition Of ◽  
Processing Techniques ◽  
Detecting Method ◽  
Gauss Distribution

In order to enhance the robustness of database watermark, a watermarking algorithm for relational database copyright protection is proposed. Some types of data are selected from the relational database according to filtering rules, and two-dimension signal is formed. Then, the watermarking is embedded into the wavelet domain of two-dimension signal. We analyze that the wavelet high frequency coefficients of corresponding data follow the Gauss distribution and give the definition of the intensive factor. Employing the linear correlation detecting method, we can embed the watermark successfully in wavelet domain. The watermark can be distributed to different parts of the relational database. Experimental results show that the embedded digital watermarks with the proposed algorithm are invisible and some degree of robustness against the commonly used database processing techniques.

Application of Wavelet Transform in the De-Noising of Angular Displacement Signals of Crank Rocker Mechanism

2014 ◽  
Vol 687-691 ◽  
pp. 1062-1065
Author(s):  
Xiao Cun Jiang ◽  
Xiao Liu ◽  
Kui Xia Han ◽  
Ji Fang Liu ◽  
Xiao Cui
Keyword(s):  
Wavelet Transform ◽  
Frequency Band ◽  
High Frequency ◽  
Angular Displacement ◽  
Low Frequency ◽  
Wavelet Domain ◽  
Test Results ◽  
High Frequency Band ◽  
Transform Method

In order to get rid of noise from the angular displacement of the crank rocker mechanism, the wavelet transform method is introduced. After the noise corresponds to the high frequency band of wavelet domain of the signal and the signal corresponds to the low frequency band of wavelet domain of the signal, the signal is decomposed into four layers, and the high frequency brand is set zero. The test results show that this method was most ideal for the de-noising effect on displacement signals, which is able to not only retain valid signals but to effectively remove the noise.

scholarly journals Image Super Resolution Based on Interpolation of Wavelet Domain High Frequency Subbands and the Spatial Domain Input Image

ETRI Journal ◽  
2010 ◽  
Vol 32 (3) ◽  
pp. 390-394 ◽  
Author(s):  
Gholamreza Anbarjafari ◽  
Hasan Demirel
Keyword(s):  
High Frequency ◽  
Super Resolution ◽  
Spatial Domain ◽  
Input Image ◽  
Wavelet Domain ◽  
Image Super Resolution

WAVELET CHARACTERIZATION OF DYADIC BMO NORM AND ITS APPLICATION IN IMAGE DECOMPOSITION FOR DISTINGUISHING BETWEEN TEXTURE AND NOISE

2011 ◽  
Vol 09 (03) ◽  
pp. 445-457 ◽  
Author(s):  
TAO ZHANG ◽  
QIBIN FAN
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Image Decomposition ◽  
Wavelet Domain ◽  
Wavelet Representation ◽  
Local Variance ◽  
Decomposition Models ◽  
Oscillating Patterns ◽  
The Relationship

Following the oscillating theory of Meyer, many image decomposition models have been proposed to split an image into two parts: structures and textures. But these models are not effective in the case of a noisy image, because both textures and noise are oscillating patterns. In this paper, we use the local variance measure to separate noise from textures. Firstly, we examine the relationship between dyadic BMO norm and local variance. Then, we give the wavelet representation of dyadic BMO norm and local variance, and further propose a method to distinguish between texture and noise in wavelet domain. In high frequency wavelet domain, we propose a decomposition model using local variance as constraints, while in low frequency domain, we use the shrinkage scheme to distinguish them. Finally, we present various numerical results on images to demonstrate the potential of our method.

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