Author Affiliations
1Shanghai Institute of Technical Physics, Chinese Academy of Science , Shanghai200083, China2University of Chinese Academy of Sciences, Beijing100049, China3CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai20008, Chinashow less
Fig. 1. classification of super-resolution reconstruction method
Fig. 2. Feature extraction results of different convolution kernels: (a) original, (b) Laplacian, (c) Laplacian + horizontal gradient, (d) Laplacian + vertical gradient, (e) Laplacian + horizontal second gradient, (f) Laplacian + vertical second gradient, (g) horizontal gradient, (h) vertical gradient, (i) horizontal second gradient, (j) vertical second gradient.
Fig. 3. Dictionary training process
Fig. 4. Comparison of sparse dictionary (a) before; (b) after
Fig. 5. Comparison of image noise: (a) noise of LR image, (b) noise of reconstruction image by 4 times
Fig. 6. Saliency algorithm based on sparse features
Fig. 7. The process of saliency regional selective super-resolution reconstruction algorithm
Fig. 8. Training dataset
Fig. 9. Comparison of reconstruction by different algorithms (a) LR image, (b) bicubic interpolation, (c)ScSR, and (d) proposed algorithm
Fig. 10. Results of super-resolution reconstruction (a)Yang algorithm, (b)SRCNN, (c) saliency-super-resolution, (d) saliency map
Fig. 11. Contrast of gray gradient value of adjacent pixels note: the x-coordinate is the x-coordinate value of the image pixels
Fig. 12. Comparison of background noise suppression results of different methods (a) LR noisy image, (b) ScSR, (c)SRCNN, (d) bicubic interpolation(BI), (e) median filtering(MF), (f) gaussian filtering(GF), (g) bilateral filtering(BF), (h) proposed algorithm(PA)
Fig. 13. SNR comparison of background noise suppression results of different methods. Note: the ordinate is the value of SNR
| 双三次插值 | ScSR | SRCNN | 本文方法 |
---|
PSNR | 30.95 | 31.39 | 32.10 | 32.15 | RMSE | 7.22 | 6.87 | 6.33 | 6.29 |
|
Table 1. 不同方法的重建结果的指标比较