DocumentCode :
3716154
Title :
Transform learning MRI with global wavelet regularization
Author :
A. Korhan Tanc;Ender M. Eksioglu
Author_Institution :
Department of EEE, Kirklareli University, Kayali, 39100, Kirklareli, Turkey
fYear :
2015
Firstpage :
1855
Lastpage :
1859
Abstract :
Sparse regularization of the reconstructed image in a transform domain has led to state of the art algorithms for magnetic resonance imaging (MRI) reconstruction. Recently, new methods have been proposed which perform sparse regularization on patches extracted from the image. These patch level regularization methods utilize synthesis dictionaries or analysis transforms learned from the patch sets. In this work we jointly enforce a global wavelet domain sparsity constraint together with a patch level, learned analysis sparsity prior. Simulations indicate that this joint regularization culminates in MRI reconstruction performance exceeding the performance of methods which apply either of these terms alone.
Keywords :
"Image reconstruction","Signal processing algorithms","Transforms","Magnetic resonance imaging","Algorithm design and analysis","Dictionaries","Noise reduction"
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2015 23rd European
Electronic_ISBN :
2076-1465
Type :
conf
DOI :
10.1109/EUSIPCO.2015.7362705
Filename :
7362705
Link To Document :
بازگشت