Medical image super-resolution via deep residual neural network in the shearlet domain

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.

Automated summary

This paper proposes an efficient medical image super-resolution method based on convolutional neural networks in the shearlet domain. The design includes building a medical image dataset, designing a new network structure for enhanced training effects, and introducing shearlet transform to address the problem of too-smooth reconstruction effects.