Abstract #0749
Compressed Sensing MRI Exploiting Complementary Dual Decomposition
Suhyung Park 1 , Chul-Ho Sohn 2 , and Jaeseok Park 1
1
Department of Brain and Cognitive
Engineering, Korea University, Seoul, Seoul, Korea,
2
Department
of Radiology, Seoul National University Hospital, Seoul,
Korea
Compressed sensing (CS) [1,2] exploits the sparsity of
an image in a transform domain. However, it has been
shown that CS suffers particularly from loss of low
contrast image features with increasing reduction
factor. To retain image details, in this work we
introduce a novel CS algorithm exploiting feature-based
complementary dual decomposition with joint estimation
of local scale mixture (LSM) model and images. Images
are decomposed into dual block sparse components: total
variation (TV) for piecewise smooth parts and wavelets
for residuals. The LSM model parameters of residuals in
the wavelet domain are estimated and then employed as a
regional constraint in spatially adaptive reconstruction
of high frequency subbands to restore image details
missing in piecewise smooth parts. Experiments
demonstrate the superior performance of the proposed
method in preserving low-contrast image features even at
high reduction factors.
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