Modeling of EEG Signals Using Wavelets with Iterative Soft Thresholding

2011 ◽  
Vol 6 (4) ◽  
pp. 37-42
Author(s):  
B.krishna Kumar ◽  
◽  
K.V.S.V.R. Prasad ◽  
K. Kishan Rao ◽  
J. Sheshagiri Babu ◽  
...  
Keyword(s):  
Eeg Signals ◽  
Soft Thresholding ◽  
Iterative Soft Thresholding

scholarly journals A guaranteed convergence analysis for the projected fast iterative soft-thresholding algorithm in parallel MRI

2021 ◽  
Vol 69 ◽  
pp. 101987
Author(s):  
Xinlin Zhang ◽  
Hengfa Lu ◽  
Di Guo ◽  
Lijun Bao ◽  
Feng Huang ◽  
...  
Keyword(s):  
Convergence Analysis ◽  
Parallel Mri ◽  
Soft Thresholding ◽  
Thresholding Algorithm ◽  
Iterative Soft Thresholding

scholarly journals A New Soft-Thresholding Image Denoising Method

2012 ◽  
Vol 6 ◽  
pp. 10-15 ◽  
Author(s):  
Mantosh Biswas ◽  
Hari Om
Keyword(s):  
Image Denoising ◽  
Denoising Method ◽  
Soft Thresholding

An Advanced Strategy for Detecting Impulses in Mechanical Signals

2005 ◽  
Vol 127 (3) ◽  
pp. 280-284 ◽  
Author(s):  
Wen-xian Yang ◽  
Peter W. Tse
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Spectral Features ◽  
Continuous Wavelet ◽  
Exponential Functions ◽  
Soft Thresholding ◽  
Thresholding Method ◽  
Mechanical Signals ◽  
Soft Threshold

The appearance of overlapping in the results derived by continuous wavelet transform (CWT) smears the spectral features and makes the results difficult to interpret. This will significantly affect the accuracy of analysis of anomalous signals. Aiming at minimizing the undesired effect of overlapping, a new soft-thresholding method in terms of exponential functions is proposed. Using the proposed soft-threshold and combining with Donoho’s approach for reducing the structures induced by noise, a strategy for purifying the results derived by the CWT is designed. A series of simulated and practical experiments show that, after adopting the proposed strategy, the results of CWT are further purified and thereby the spectral features of the inspected signal become more explicit and much more easily identified.

Soft thresholding by noise invalidation

2008 ◽  
Author(s):  
Soosan Beheshti ◽  
Nima Nikvand ◽  
Xavier N. Fernando
Keyword(s):  
Soft Thresholding

Electrocardiogram signal denoising by clustering and soft thresholding

2018 ◽  
Vol 12 (9) ◽  
pp. 1165-1171 ◽  
Author(s):  
Regis Nunes Vargas ◽  
Antônio Cláudio Paschoarelli Veiga
Keyword(s):  
Signal Denoising ◽  
Soft Thresholding ◽  
Electrocardiogram Signal

Denoising Ultrasound Medical Images

2017 ◽  
pp. 761-775
Author(s):  
A.S.C.S. Sastry ◽  
P.V.V. Kishore ◽  
Ch. Raghava Prasad ◽  
M.V.D. Prasad
Keyword(s):  
Image Quality ◽  
Medical Images ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Soft Thresholding ◽  
Block Based

Medical ultrasound imaging has revolutioned the diagnostics of human body in the last few decades. The major drawback of ultrasound medical images is speckle noise. Speckle noise in ultrasound images is because of multiple reflections of ultrasound waves from hard tissues. Speckle noise degrades the medical ultrasound images lessening the visible quality of the image. The aim of this paper is to improve the image quality of ultrasound medical images by applying block based hard and soft thresholding on wavelet coefficients. Medical ultrasound image transformation to wavelet domain uses debauchee's mother wavelet. Divide the approximate and detailed coefficients into uniform blocks of size 8×8, 16×16, 32×32 and 64×64. Hard and soft thresholding on these blocks of approximate and detailed coefficients reduces speckle noise. Inverse transformation to original spatial domain produces a noise reduced ultrasound image. Experiments on medical ultrasound images obtained from diagnostic centers in Vijayawada, India show good improvements to ultrasound images visually. Quality of improved images in measured using peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index (SSIM).

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