Investigations of SEB, STAB and optical flow on T2 for SR 4x.

Investigations of SEB, STAB and optical flow on T2 for SR 4x.</p

Baseline comparison on PD for SR 4x (PSNR/SSIM).

Baseline comparison on PD for SR 4x (PSNR/SSIM).</p

The testing performance of different configurations of projection units and the convolutional layers of projection unit on PD for SR 4x.

The testing performance of different configurations of projection units and the convolutional layers of projection unit on PD for SR 4x.</p

Quantitative comparison between the state-of-the-art SR algorithms on 3 test datasets.

Quantitative comparison between the state-of-the-art SR algorithms on 3 test datasets.</p

The performance comparison between the model with different number of slices, 4x SR on PD.

MSBFAN/s: MSBFAN trained/tested with s slices. Note: MSBFAN/1 equivalent MSBPN (n = 1, m = 2).</p

Overview of the proposed MSBFAN for sequence MR image SR.

The overall structure consists of three parts: Initial feature extraction FE(.), spatio-temporal module FSTAM(.) and reconstruction FR(.). The horizontal line is based on our MSBPN to explore the spatial information of target slice. The vertical line computes the residual features from a pair of target and neighbor slices to explore the temporal information. On each spatio-temporal attention module, the spatial information and the temporal information are connected and enhanced to recover the missing details.</p

Test results of the models with different connection approximations on T1 for SR 4x (PSNR/SSIM).

Test results of the models with different connection approximations on T1 for SR 4x (PSNR/SSIM).</p

The diagram of the proposed MSBPN model.

The overall structure consists of two main parts: cascaded projections of different scales and a channel selection layer. Each scale projection module is composed of k sub-projection modules that employ densely connected to encourage feature reuse. Each sub-projection module contains j alternating up- and down-layer group to generate the projection error and iteratively refine the LR.</p

Test results of the model with multi-scale configuration on T2 for SR 4x (PSNR/Parameters).

Test results of the model with multi-scale configuration on T2 for SR 4x (PSNR/Parameters).</p

The performance comparison between different configures of our MSBPN for SR 4x.

(a) Impact of projection units and the convolutional layers of the projection unit validated on PD. (b) Dense Connection, validated on T1. (c) Multi-scale machine, validated on T2.</p