TY - GEN
T1 - Shearlet-based sparse representation for super-resolution in diffusion weighted imaging (DWI)
AU - Tarquino, Jonathan
AU - Rueda, Andrea
AU - Romero, Eduardo
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/28
Y1 - 2014/1/28
N2 - Diffusion Weighted (DW) imaging have proven to be useful in brain architectural analyses and in research about the brain tract organization and neuronal connectivity. However, the clinical use of DW images is currently limited by a series of acquisition artifacts, such as the partial volume effect (PVE), that affect the spatial resolution, and therefore, the sensitivity of further DW imaging analysis. In this paper, a new superresolution method is presented, given the redundancy present in this kind of images. The proposed method uses local information and a multiscale Shearlet transformation to represent the directional features and the spectral content of the DW images. A comparison of this proposal with a classical image interpolation method demonstrates an improvement of about 3 dB in the PSNR measure and 4.5% in the SSIM metric.
AB - Diffusion Weighted (DW) imaging have proven to be useful in brain architectural analyses and in research about the brain tract organization and neuronal connectivity. However, the clinical use of DW images is currently limited by a series of acquisition artifacts, such as the partial volume effect (PVE), that affect the spatial resolution, and therefore, the sensitivity of further DW imaging analysis. In this paper, a new superresolution method is presented, given the redundancy present in this kind of images. The proposed method uses local information and a multiscale Shearlet transformation to represent the directional features and the spectral content of the DW images. A comparison of this proposal with a classical image interpolation method demonstrates an improvement of about 3 dB in the PSNR measure and 4.5% in the SSIM metric.
KW - Shearlet transform
KW - Super-resolution
KW - information redundancy
KW - point-spread function
KW - sparse representation
UR - http://www.scopus.com/inward/record.url?scp=84949927266&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2014.7025791
DO - 10.1109/ICIP.2014.7025791
M3 - Conference contribution
AN - SCOPUS:84949927266
T3 - 2014 IEEE International Conference on Image Processing, ICIP 2014
SP - 3897
EP - 3900
BT - 2014 IEEE International Conference on Image Processing, ICIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -