Noise reduction of echocardiographic images using wavelet filtering

2002 ◽  
Author(s):  
Su Cheol Kang ◽  
Sang Min Lee ◽  
Seung Hong Hong
Keyword(s):  
Noise Reduction ◽  
Wavelet Filtering ◽  
Echocardiographic Images

Speckle noise reduction in echocardiographic images of aortic valve and cardiac chambers

Optik ◽  
2015 ◽  
Vol 126 (2) ◽  
pp. 153-163 ◽  
Author(s):  
Nagashettappa Biradar ◽  
M.L. Dewal ◽  
Manoj Kumar Rohit
Keyword(s):  
Aortic Valve ◽  
Noise Reduction ◽  
Speckle Noise ◽  
Echocardiographic Images ◽  
Speckle Noise Reduction

Speckle Noise Reduction in B-Mode Echocardiographic Images: A comparison

2015 ◽  
Vol 32 (6) ◽  
pp. 435-453 ◽  
Author(s):  
Nagashettappa Biradar ◽  
Mohan Lal Dewal ◽  
Manoj Kumar Rohit
Keyword(s):  
Noise Reduction ◽  
Speckle Noise ◽  
Echocardiographic Images ◽  
Speckle Noise Reduction

scholarly journals CMB Map Restoration

2012 ◽  
Vol 2012 ◽  
pp. 1-15
Author(s):  
J. Bobin ◽  
J.-L. Starck ◽  
F. Sureau ◽  
J. Fadili
Keyword(s):  
Spatial Variability ◽  
Noise Reduction ◽  
Spherical Harmonics ◽  
Wiener Filter ◽  
Microwave Background ◽  
Wavelet Filtering ◽  
Planck Data ◽  
Sky Surveys ◽  
Filtering Technique ◽  
Non Gaussian

Estimating the cosmological microwave background is of utmost importance for cosmology. However, its estimation from full-sky surveys such as WMAP or more recently Planck is challenging: CMB maps are generally estimated via the application of some source separation techniques which never prevent the final map from being contaminated with noise and foreground residuals. These spurious contaminations whether noise or foreground residuals are well known to be a plague for most cosmologically relevant tests or evaluations; this includes CMB lensing reconstruction or non-Gaussian signatures search. Noise reduction is generally performed by applying a simple Wiener filter in spherical harmonics; however, this does not account for the non-stationarity of the noise. Foreground contamination is usually tackled by masking the most intense residuals detected in the map, which makes CMB evaluation harder to perform. In this paper, we introduce a novel noise reduction framework coined LIW-Filtering for Linear Iterative Wavelet Filtering which is able to account for the noise spatial variability thanks to a wavelet-based modeling while keeping the highly desired linearity of the Wiener filter. We further show that the same filtering technique can effectively perform foreground contamination reduction thus providing a globally cleaner CMB map. Numerical results on simulated Planck data are provided.

Satisfaction With Noise Reduction Circuits

1993 ◽  
Vol 2 (1) ◽  
pp. 51-53 ◽  
Author(s):  
Ruth A. Bentler
Keyword(s):  
Noise Reduction
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