Wavelet Digital Watermarking Algorithm on the Basis of SVD Decomposition

2013 ◽  
Vol 333-335 ◽  
pp. 1056-1059 ◽  
Author(s):  
Ji Lin Wang
Keyword(s):  
Correlation Function ◽  
Wavelet Transform ◽  
Digital Watermarking ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Intermediate Frequency ◽  
Singular Value ◽  
Signal To Noise ◽  
Value Decomposition ◽  
Svd Decomposition

Based on SVD decomposition, an digital watermarking algorithm with the method of wavelet transform is proposed. By means of singular value decomposition to realize blind extracting, the watermarking image is embedded into intermediate frequency sub bands of wavelet composition. Finally, applying normalized cross-correlation function (NC) and peak signal to noise ratio (PSNC), it has proved that this algorithm has better invisibility and robustness.

scholarly journals De-Noising of Magnetotelluric Signals by Discrete Wavelet Transform and SVD Decomposition

2021 ◽  
Vol 13 (23) ◽  
pp. 4932
Author(s):  
Rui Zhou ◽  
Jiangtao Han ◽  
Zhenyu Guo ◽  
Tonglin Li
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Large Scale ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Response Curves ◽  
Noise Ratio ◽  
Value Decomposition ◽  
Svd Decomposition

Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise in MT data based on discrete wavelet transform and singular value decomposition (SVD), with multiscale dispersion entropy and phase space reconstruction carried out for pretreatment. No “over processing” takes place in the proposed method. Compared with wavelet transform and SVD decomposition in synthetic tests, the proposed method removes the profile of noise more completely, including large-scale noise and impulse noise. For high levels or low levels of noise, the proposed method can increase the signal-to-noise ratio of data more obviously. Moreover, application to the field MT data can prove the performance of the proposed method. The proposed method is a feasible method for the elimination of various noise types and can improve MT data with high noise levels, obtaining a recovery in the response. It can improve abrupt points and distortion in MT response curves more effectively than the robust method can.

scholarly journals Determination of Broadening Functions using the Singular Value Decomposition (SVD) Technique

1999 ◽  
Vol 170 ◽  
pp. 82-90 ◽  
Author(s):  
Slavek Rucinski
Keyword(s):  
Correlation Function ◽  
Singular Value Decomposition ◽  
Cross Correlation ◽  
Singular Value ◽  
Spectral Lines ◽  
Linear Inversion ◽  
Standard Tool ◽  
Contact Binaries ◽  
Value Decomposition

AbstractThe cross-correlation function (CCF) has become the standard tool for extraction of radial-velocity and broadening information from high resolution spectra. It permits integration of information which is common to many spectral lines into one function which is easy to calculate, visualize and interpret. However, the CCF is not the best tool for many applications where it should be replaced by the proper broadening function (BF). Typical applications requiring use of BFs rather than CCFs involve finding locations of star spots, studies of projected shapes of highly distorted stars such as contact binaries (as no assumptions can be made about BF symmetry or even continuity) and [Fe/H] metallicity determinations (good baselines and avoidance of negative lobes are essential). It is stressed that the CCFs are not broadening functions. This note concentrates on the advantages of determining BFs through the process of linear inversion, preferably accomplished using the singular value decomposition (SVD). Some basic examples of numerical operations are given in the IDL programming language.

scholarly journals Receiver phase alignment using fitted SVD derived sensitivities from routine prescans

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256700
Author(s):  
Olivia W. Stanley ◽  
Ravi S. Menon ◽  
L. Martyn Klassen
Keyword(s):  
Singular Value Decomposition ◽  
Radio Frequency ◽  
Signal To Noise Ratio ◽  
Singular Value ◽  
7 Tesla ◽  
Signal To Noise ◽  
Phase Signal ◽  
Noise Ratio ◽  
Value Decomposition ◽  
Svd Method

Magnetic resonance imaging radio frequency arrays are composed of multiple receive coils that have their signals combined to form an image. Combination requires an estimate of the radio frequency coil sensitivities to align signal phases and prevent destructive interference. At lower fields this can be accomplished using a uniform physical reference coil. However, at higher fields, uniform volume coils are lacking and, when available, suffer from regions of low receive sensitivity that result in poor sensitivity estimation and combination. Several approaches exist that do not require a physical reference coil but require manual intervention, specific prescans, or must be completed post-acquisition. This makes these methods impractical for large multi-volume datasets such as those collected for novel types of functional MRI or quantitative susceptibility mapping, where magnitude and phase are important. This pilot study proposes a fitted SVD method which utilizes existing combination methods to create a phase sensitive combination method targeted at large multi-volume datasets. This method uses any multi-image prescan to calculate the relative receive sensitivities using voxel-wise singular value decomposition. These relative sensitivities are fitted to the solid harmonics using an iterative least squares fitting algorithm. Fits of the relative sensitivities are used to align the phases of the receive coils and improve combination in subsequent acquisitions during the imaging session. This method is compared against existing approaches in the human brain at 7 Tesla by examining the combined data for the presence of singularities and changes in phase signal-to-noise ratio. Two additional applications of the method are also explored, using the fitted SVD method in an asymmetrical coil and in a case with subject motion. The fitted SVD method produces singularity-free images and recovers between 95–100% of the phase signal-to-noise ratio depending on the prescan data resolution. Using solid harmonic fitting to interpolate singular value decomposition derived receive sensitivities from existing prescans allows the fitted SVD method to be used on all acquisitions within a session without increasing exam duration. Our fitted SVD method is able to combine imaging datasets accurately without supervision during online reconstruction.

Review on Singular-Value-Decomposition-Based Digital Watermarking Algorithm in Combination with Image Features

2012 ◽  
Vol 424-425 ◽  
pp. 452-463
Author(s):  
Tong Tong Liu ◽  
Cheng Yang
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Comparative Analysis ◽  
Singular Value Decomposition ◽  
Digital Watermarking ◽  
Image Features ◽  
Singular Value ◽  
Experimental Results ◽  
Processing Effects ◽  
Value Decomposition

The combination of image features with singular value decomposition algorithm and digital watermarking algorithm based on wavelet transform respectively makes possible the advancement from pixel watermarking to content watermarking, which effectively solves the contradiction between perceptibility and robustness of conventional watermarking algorithm. Therefore, stronger robustness and lower perceptibility of watermarking are achieved. This paper first gives a review on conventional singular-value-decomposition-based digital watermarking algorithm, and then makes a thorough analysis of its respective features, advantages and defects. Improved feature extraction schemes of digital watermark which combine image features with SVD algorithm as well as the application algorithm in feature extraction based on improved SVD algorithm are put forward for the comparative analysis of corresponding principles and processing effects. The experimental results indicate that the content-based (instead of pixel-based) watermarking algorithm can better satisfy the perceptibility and robustness of digital watermarking, with huge application potentials and more development space

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