Laser & Optoelectronics Progress, Volume. 56, Issue 4, 041004(2019)
Backscattered Light Repairing Method for Underwater Laser Image Based on Improved Generative Adversarial Network
Figures & Tables(14)
Fig. 2. Schematic of training set. (a) Sample image; (b) image with backscattered light; (c) image with mixed noise
Fig. 6. Processing results of noise parameter (0, 20 dB, 0.01). (a) Target image; (b) image to be repaired; (c) Denoise+DCP; (d) Denoise+HEMSRCR; (e) proposed method
Fig. 7. Processing results of noise parameter (0, 25 dB, 0.015). (a) Target image; (b) image to be repaired; (c) Denoise+DCP; (d) Denoise+HEMSRCR; (e) proposed method
Fig. 8. Processing results of noise parameter (0, 30 dB, 0.02). (a) Target image; (b) image to be repaired; (c) Denoise+DCP; (d) Denoise+HEMSRCR; (e) proposed method
Table 1. Detailed configuration information of the generator networkpixel
View tableTable 1. Detailed configuration information of the generator networkpixel
Name Kernel size Stride Dilation rate Output size BN Dropout input - - - 256×256×1 - - conv1…conv2 conv3 64×3×3 128×3×3 1 2 1 1 256×256×64 128×128×128 P P - - conv4 128×3×3 1 1 128×128×128 P - conv5 256×3×3 2 1 64×64×256 P - conv6…conv7 dilaconv8 256×3×3 256×3×3 1 1 1 2 64×64×256 64×64×256 P P - - dilaconv9 256×3×3 1 4 64×64×256 P - dilaconv10 256×3×3 1 8 64×64×256 P - dilaconv11 256×3×3 1 16 64×64×256 P - conv12…conv13 256×3×3 1 1 64×64×256 P - transconv14 128×4×4 2 - 128×128×128 P - merge1(conv4 + transconv14) - - - 128×128×256 - P conv15 128×3×3 1 1 128×128×128 P - transconv16 64×4×4 2 1 256×256×64 P - merge2(conv2 + transconv16) - - - 256×256×128 - P conv17 32×3×3 1 1 256×256×32 P - output 1×3×3 1 1 256×256×1 - -
Table 2. Detailed configuration information of the discriminator networkpixel
View tableTable 2. Detailed configuration information of the discriminator networkpixel
Name Kernel size Stride Dilation rate Output size BN Dropout Input - - - 256×256×1 - - Conv1 Conv2 64×5×5 128×5×5 2 2 1 1 128×128×64 64×64×128 P P - - Conv3 256×5×5 2 1 32×32×256 P - Conv4 512×5×5 2 1 16×16×512 P - Conv5 512×5×5 2 1 8×8×512 P - Conv6 512×5×5 2 1 4×4×512 P - FC - - - 1 - -
Table 3. Structure of data set
View tableTable 3. Structure of data set
Item Train set Validation set Test set Number of images 5850 650 40 40 40 Size / (pixel×pixel) 256× 256 256× 256 256× 256 512× 512 960× 960
Table 4. The PSNR of different test imagesdB
View tableTable 4. The PSNR of different test imagesdB
Image Denoise Denoise+DCP Denoise+ HEMSRCR Proposed method Lv1 Lv2 Lv3 Lv1 Lv2 Lv3 Lv1 Lv2 Lv3 Lv1 Lv2 Lv3 1 16.94 16.96 16.90 10.67 10.75 10.74 6.05 6.07 6.06 22.61 22.31 21.50 2 14.78 14.80 14.76 8.60 8.67 8.69 5.93 5.96 5.98 21.97 19.14 21.15 3 11.86 11.93 11.98 4.55 4.57 4.61 5.51 5.52 5.51 17.38 20.65 18.24 4 13.40 13.45 13.47 6.92 7.06 7.09 5.67 5.70 5.71 18.52 18.64 17.81 5 12.33 12.36 12.39 5.61 5.74 5.77 5.56 5.57 5.59 10.40 10.27 9.50 6 15.11 15.14 15.18 8.65 8.76 8.84 5.84 5.88 5.89 20.72 17.24 20.49
Table 5. The FSIM of different test imagesdB
View tableTable 5. The FSIM of different test imagesdB
Image Denoise Denoise+DCP Denoise+ HEMSRCR Proposed method Lv1 Lv2 Lv3 Lv1 Lv2 Lv3 Lv1 Lv2 Lv3 Lv1 Lv2 Lv3 1 0.91 0.91 0.91 0.90 0.91 0.91 0.59 0.58 0.53 0.93 0.92 0.92 2 0.88 0.88 0.89 0.88 0.88 0.89 0.61 0.60 0.57 .90 0.91 0.90 3 0.92 0.91 0.90 0.90 0.91 0.92 0.65 0.62 0.55 0.92 0.91 0.91 4 0.87 0.87 0.88 0.87 0.87 0.88 0.61 0.59 0.56 0.92 0.89 0.89 5 0.89 0.89 0.90 0.89 0.89 0.90 0.68 0.66 0.60 0.91 0.89 0.88 6 0.89 0.89 0.90 0.89 0.89 0.90 0.60 0.59 0.55 0.93 0.91 0.91
Table 6. Time of different methods
View tableTable 6. Time of different methods
Image size (pixel×pixel) Denoise Denoise+DCP Denoise+HEMSRCR Proposed method Speed up 256×256 0.3227 0.3311 0.6152 0.0460 9.20× 512×512 1.4841 1.5059 2.3453 0.2561 6.94× 960×960 5.6027 5.6854 9.5682 0.4109 16.91×
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Qingbo Zhang, Xiaohui Zhang, Hongwei Han. Backscattered Light Repairing Method for Underwater Laser Image Based on Improved Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041004
Paper Information
Category: Image Processing
Received: Aug. 23, 2018
Accepted: Sep. 6, 2018
Published Online: Jul. 31, 2019
The Author Email: Qingbo Zhang (527992400@qq.com)
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