A wavelet transformation denoising method based on singular value decomposition

2017 ◽  
Author(s):  
Xiu-Lin Sui ◽  
Yan Jiao ◽  
Jiang-Hua Ge ◽  
Xiao-Qi Chen
Keyword(s):  
Singular Value Decomposition ◽  
Wavelet Transformation ◽  
Singular Value ◽  
Denoising Method ◽  
Value Decomposition

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

scholarly journals Medical Image Authentication using Stationary Wavelet Transformation and Singular Value Decomposition

2019 ◽  
Vol 8 (3) ◽  
pp. 953-958
Keyword(s):  
Singular Value Decomposition ◽  
Medical Image ◽  
Wavelet Transformation ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Image Authentication ◽  
Transformation Method ◽  
Singular Value ◽  
Invariance Property ◽  
Value Decomposition

he proposed paper work is implemented using Stationary Wavelet Transformation (SWT) with Singular Value Decomposition (SVD).Even though, there are many other transformations, the Stationary Wavelet Transformation method is chosen for its shift invariance property. The designed method has three steps; the first step is the decomposing of the Medical image into sub-bands using SWT to find the value of sub band and as a second step is to apply SVD, third step will combine both the images with scaling factor. The experiments were conducted over gray scale of MRI and CT Medical images. The statistics of proposed method indicates that imperceptibility of Watermarked Medical images have a Peak Signal to Noise Ratio (PSNR) value of 50 DB for medical images. The robustness is ensured by having Correlation Coefficient (CC) of 1 for the retrieved watermark images. Security for the watermark is extended by encrypting the watermark with chaotic sequence.

scholarly journals An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3465 ◽  
Author(s):  
Kai Zhou ◽  
Mingzhi Li ◽  
Yuan Li ◽  
Min Xie ◽  
Yonglu Huang
Keyword(s):  
Singular Value Decomposition ◽  
White Noise ◽  
Minimum Description Length ◽  
Partial Discharge ◽  
Singular Values ◽  
Singular Value ◽  
Denoising Method ◽  
Signal Segment ◽  
Value Decomposition ◽  
Short Time

To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a denoising method for PD signals, named adaptive short-time singular value decomposition (ASTSVD). First, a sliding window was moved along the time axis of a PD signal to cut a whole signal into segments with overlaps. The singular value decomposition (SVD) method was then applied to each segment to obtain its singular value sequence. The minimum description length (MDL) criterion was used to determine the number of effective singular values automatically. Then, the selected singular values of each signal segment were used to reconstruct the noise-free signal segment, from which the denoised PD signal was obtained. To evaluate ASTSVD, we applied ASTSVD and two other methods on simulated, laboratory-measured, and field-detected noisy PD signals, respectively. Compared to the other two methods, the denoised PD signals of ASTSVD contain less residual noise and exhibit smaller waveform distortion.

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