Chao Sun, Junwei Lü, Jianwei Li, Rongchao Qiu. Method of Rapid Image Super-Resolution Based on Deconvolution[J]. Acta Optica Sinica, 2017, 37(12): 1210004
- Acta Optica Sinica
- Vol. 37, Issue 12, 1210004 (2017)
Fig. 1. Network architectures of image super-resolution methods based on deep-learning. (a) SRCNN; (b) VDSR; (c) DRCN; (d) ESPCN; (e) FSRCNN; (f) RSRD
Fig. 2. PSNR versus calculation time for different methods performing super-resolution over Set14 with magnification factor of 3
Fig. 3. Trend of mean PSNR of dataset with iteration rising under different layers. (a) Set5; (b) Set14
Fig. 4. Trend of the mean PSNR of dataset with iteration rising under different deconvolution kernel sizes. (a) Set5; (b) Set14
Fig. 5. Trend of the mean PSNR of dataset with iteration rising under different active functions. (a) Set5; (b) Set14
Fig. 6. Proposed training network applied to magnification factor of 3
Fig. 7. [in Chinese]
Fig. 8. Whole and local comparisons of foreman.bmp in Set14 processed by different methods with magnification factor of 4. (a) Real image; (b) Bicubic(29.57dB); (c) ANR(30.80 dB); (d) SRCNN(31.50 dB); (e) A+(32.20 dB); (f) ESPCN(32.02 dB); (g) FSRCNN-s(31.52 dB); (h) RSRD(32.89 dB)
Fig. 9. Whole and local comparisons of real image processed by different methods with magnification factor of 4. (a) Real image; (b) Bicubic(29.88 dB); (c) ANR(30.90 dB); (d) SRCNN(31.65 dB); (e) A+(31.30 dB); (f) ESPCN(31.85 dB); (g) FSRCNN-s(31.85 dB); (h) RSRD(32.47 dB)
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Table 1. Mean PSNR of test datasets processed by all methods with different magnification factors trained by 91 images
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Table 2. Mean PSNR of test datasets processed by all methods with different magnification factors trained by 291 images
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Table 3. Comparisons between the proposed RSRD and other methods
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