Application of the Digital Curvelet Transform for the Purpose of Image Denoising in MRI

2014 ◽  
pp. 165-173 ◽  
Author(s):  
Joanna Świebocka-Więk ◽  
Henryk Figiel
Keyword(s):  
Image Denoising ◽  
Curvelet Transform

Medical Image Denoising With Various Noise Estimators Using Curvelet Transform

2014 ◽  
Vol 1 (2) ◽  
pp. 30-40
Author(s):  
Abha Choubey ◽  
◽  
Dr.G.R. Sinha ◽  
S. K Naik ◽  
◽  
...  
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Curvelet Transform

Image denoising based on fast discrete curvelet transform

2009 ◽  
Vol 28 (12) ◽  
pp. 3138-3140
Author(s):  
Gao-qiu FANG ◽  
Zheng-yong WANG ◽  
Xiao-hong WU
Keyword(s):  
Image Denoising ◽  
Curvelet Transform ◽  
Fast Discrete Curvelet Transform

Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform

Optik ◽  
2018 ◽  
Vol 159 ◽  
pp. 333-343 ◽  
Author(s):  
Sidheswar Routray ◽  
Arun Kumar Ray ◽  
Chandrabhanu Mishra
Keyword(s):  
Image Denoising ◽  
Curvelet Transform ◽  
Bilateral Filter

Poisson image denoising using fast discrete curvelet transform and wave atom

2012 ◽  
Vol 92 (9) ◽  
pp. 2002-2017 ◽  
Author(s):  
Sandeep Palakkal ◽  
K.M.M. Prabhu
Keyword(s):  
Image Denoising ◽  
Curvelet Transform ◽  
Fast Discrete Curvelet Transform

The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules

2012 ◽  
pp. 227-235 ◽  
Author(s):  
D. Selvathi ◽  
S. Thamarai Selvi ◽  
C. Loorthu Sahaya Malar
Keyword(s):  
Image Denoising ◽  
Mean Squared Error ◽  
Diffusion Tensor ◽  
Curvelet Transform ◽  
Research Area ◽  
Magnetic Resonance Images ◽  
Image Domain ◽  
Active Research ◽  
Unbiased Risk ◽  
Wavelet Image

SURE-LET Approach is used for reducing or removing noise in brain Magnetic Resonance Images (MRI). Removing or reducing noise is an active research area in image processing. Rician noise is the dominant noise in MRIs. Due to this type of noise, the abnormal tissue (cancerous tissue) may be misclassified as normal tissue and introduces bias into MRI measurements that can have significant impact on the shapes and orientations of tensors in diffusion tensor MRIs. SURE is a new approach to Orthonormal wavelet image denoising. It is an image-domain minimization of an estimate of the mean squared error—Stein’s unbiased risk estimates (SURE). Here, the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Different Shrinkage functions such as Soft and Hard and Shrinkage rules and Universal and BayesShrink are used to remove noise and the performance of these results are compared. The algorithm is applied on brain MRIs with different noisy conditions by varying standard deviation of noise. The performance of this approach is compared with performance of the Curvelet transform.

New image denoising method based on Curvelet transform

2009 ◽  
Vol 29 (10) ◽  
pp. 2665-2667
Author(s):  
Dan LI ◽  
Jian-sheng QIAN ◽  
Chao WANG
Keyword(s):  
Image Denoising ◽  
Curvelet Transform ◽  
Denoising Method
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