Laser & Optoelectronics Progress, Volume. 55, Issue 12, 121006(2018)
Image Super-Resolution Method Combining Wavelet Transform with Deep Network
Fig. 4. Network architecture of DRRN. (a) Hole architecture; (b) structure of recursive block
Fig. 5. Structure of modified recursive block. (a) Removing BN layers; (b) decreasing convolution layers
Fig. 7. Whole and local comparisons of “monarch.bmp” in Set14 processed with scale ×3. (a) Real image; (b) Bicubic/29.43 dB; (c) SRCNN/32.39 dB; (d) FSRCNN/32.44 dB; (e) VDSR/34.51 dB; (f) DRRN/34.68 dB; (g) proposed SDN/34.53 dB; (h) proposed CWTDN/34.82 dB
Fig. 8. Whole and local comparisons of “21077.bmp” in BSD100 processed with scale ×4. (a) Real image; (b) Bicubic/24.88 dB; (c) SRCNN/25.84 dB; (d) FSRCNN/26.13 dB; (e) VDSR/26.57 dB; (f) DRRN/26.75 dB; (g) proposed SDN/26.60 dB; (h) proposed CWTDN/26.91 dB
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Chao Sun, Junwei Lü, Jian Gong, Rongchao Qiu, Jianwei Li, Heng Wu. Image Super-Resolution Method Combining Wavelet Transform with Deep Network[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121006
Category: Image Processing
Received: May. 7, 2018
Accepted: Jun. 20, 2018
Published Online: Aug. 1, 2019
The Author Email: Chao Sun (lemony1314@163.com), Junwei Lü (ljwei369@163.com)