Ming Zhang, Xiaoqi Lü, Liang Wu, Dahua Yu. Multiplicative Denoising Method Based on Deep Residual Learning[J]. Laser & Optoelectronics Progress, 2018, 55(3): 031004
- Laser & Optoelectronics Progress
- Vol. 55, Issue 3, 031004 (2018)
Fig. 1. Architecture of residual learning
Fig. 2. Architecture of the CNN
Fig. 3. Test images used in the experiment
Fig. 4. Results of different methods for test images under the same noise level. (a) Noise images; (b) Lee method; (c) Frost method; (d) BM3D method; (e) NL method; (f) CNN method
Fig. 5. Detail comparison of different denoising methods.(a) Lee method; (b) Frost method; (c) BM3D method; (d) NL method; (e) CNN method
Fig. 6. Denoising results of CNN method under different noise levels. (a) σ2=0.02; (b) σ2=0.04;(c) σ2=0.06; (d) σ2=0.08; (e) σ2=0.1
Fig. 7. (a) PSNR and (b) SSIM of different denoising methods versus noise level
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Table 1. PSNR of different methods for test images under the same noise leveldB
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Table 2. SSIM of different methods for test images under the same noise level
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Table 3. PSNR of different methods under different noise levelsdB
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Table 4. SSIM of different methods under different noise levels
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