Mengxue Pan, Ning Qu, Yeru Xia, Deyong Yang, Hongyu Wang, Wenlong Liu. Super-Resolution Reconstruction of Magnetic Resonance Image Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2217001
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2217001 (2022)
Fig. 1. Network structure
Fig. 2. Convolution residual block and residual block. (a) Convolution residual block; (b) residual block
Fig. 3. Sub-pixel convolution
Fig. 4. Image 1 reconstruction results with different algorithms. (a) Original image; (b) Bicubic; (c) FSRCNN; (d) EDSR; (e) SRResNet; (f) proposed algorithm
Fig. 5. Image 2 reconstruction results with different algorithms. (a) Original image; (b) Bicubic; (c) FSRCNN; (d) EDSR; (e) SRResNet; (f) proposed algorithm
Fig. 6. Image 3 reconstruction results with different algorithms. (a) Original image; (b) Bicubic; (c) FSRCNN; (d) EDSR; (e) SRResNet; (f) proposed algorithm
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Table 1. Convolution residual block parameter setting
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Table 2. Image subjective evaluation form
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Table 3. Comparison of subjective evaluation values of super-resolution reconstruction methods
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Table 4. Comparison of PSNR values of various super-resolution reconstruction
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Table 5. Comparison of energy gradient values of various super-resolution reconstruction
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