Medical image super-resolution via deep residual neural network in the shearlet domain
Document Type
Article
Publication Date
7-1-2021
Abstract
This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details.
Identifier
85105538112 (Scopus)
Publication Title
Multimedia Tools and Applications
External Full Text Location
https://doi.org/10.1007/s11042-021-10894-0
e-ISSN
15737721
ISSN
13807501
First Page
26637
Last Page
26655
Issue
17
Volume
80
Grant
2019GXRC031
Fund Ref
Department of Science and Technology of Shandong Province
Recommended Citation
Wang, Chunpeng; Wang, Simiao; Xia, Zhiqiu; Li, Qi; Ma, Bin; Li, Jian; Yang, Meihong; and Shi, Yun Qing, "Medical image super-resolution via deep residual neural network in the shearlet domain" (2021). Faculty Publications. 3999.
https://digitalcommons.njit.edu/fac_pubs/3999
