Laser & Optoelectronics Progress, Volume. 58, Issue 14, 1410002(2021)
Dual Residual Denoising Network Based on Hybrid Attention
Figures & Tables(17)
Fig. 2. Structure of ResNet block and SE-ResNet block. (a) Traditional ResNet block; (b) SE-ResNet block
Fig. 8. Structure of ResNet block. (a) Traditional ResNet block; (b) simplified ResNet block
Fig. 11. Denoised images of different algorithms on Starfish (σ=30). (a) Original image; (b) noisy image; (c) DnCNN; (d) FDnCNN; (e) FFDNet; (f) IRCNN; (g) DuRN; (h) HDDNet
Fig. 12. Denoised images of different algorithms on Butterfly (σ=50). (a) Original image; (b) noisy image; (c) DnCNN; (d) FDnCNN; (e) FFDNet; (f) IRCNN; (g) DuRN; (h) HDDNet
Fig. 13. Denoised images for pepper & salt noise (noise density is 10%). (a)--(g) Pepper & salt noise images; (h)--(n) denoised images
Table 1. Parameters of DNL-block and D-block modules
View tableTable 1. Parameters of DNL-block and D-block modules
Module Conv-A kernel size Conv-B kernel size Dilation DNL-block1 5 3 1 D-block1 7 5 1 D-blcok2 7 5 2 D-block3 11 7 2 D-block4 11 5 1 DNL-block2 11 7 3
Table 2. PSNR values of different methods
View tableTable 2. PSNR values of different methods
Noise level Method Cameraman House Peppers Starfish Butterfly Airplane Parrot Average σ=15 DnCNN 32.59 34.99 33.24 32.13 33.25 31.67 31.88 32.82 FDnCNN 32.59 35.13 33.17 32.07 33.14 31.67 31.85 32.80 FFDNet 32.37 35.05 33.02 31.95 32.92 31.55 31.79 32.66 IRCNN 32.53 34.88 33.21 31.96 32.98 31.66 31.88 32.73 DuRN 32.29 34.73 33.02 32.01 32.95 31.62 31.80 32.63 HDDNet 32.59 34.96 33.21 32.15 33.22 31.69 31.86 32.81 σ=30 DnCNN 29.28 32.29 29.85 28.28 29.42 28.19 28.59 29.42 FDnCNN 29.43 32.59 29.92 28.37 29.43 28.21 28.70 29.52 FFDNet 29.28 32.57 29.87 28.34 29.39 28.13 28.65 29.46 IRCNN 29.28 32.19 29.79 28.14 29.22 28.14 28.62 29.34 DuRN 28.99 32.26 29.53 28.35 29.24 28.08 28.52 29.28 HDDNet 29.43 32.43 29.98 28.56 29.52 28.23 28.65 29.53 σ=50 DnCNN 27.26 29.96 27.35 25.6 26.83 25.83 26.42 27.04 FDnCNN 27.35 30.25 27.41 25.66 26.84 25.81 26.59 27.13 FFDNet 27.34 30.36 27.41 25.68 26.92 25.79 26.57 27.14 IRCNN 27.16 29.90 27.33 25.48 26.66 25.78 26.48 26.97 DuRN 26.91 30.14 27.13 25.71 26.73 25.87 26.35 26.98 HDDNet 27.33 30.35 27.37 25.89 26.97 25.90 26.47 27.18
Table 3. SSIM values of different methods
View tableTable 3. SSIM values of different methods
Noise level Method Cameraman House Peppers Starfish Butterfly Airplane Parrot Average σ=15 DnCNN 0.9131 0.8855 0.9121 0.9146 0.9501 0.9077 0.9049 0.9126 FDnCNN 0.9132 0.8870 0.9119 0.9135 0.9503 0.9080 0.9047 0.9127 FFDNet 0.9118 0.8877 0.9112 0.9126 0.9491 0.9074 0.9045 0.9120 IRCNN 0.9113 0.8831 0.9107 0.9123 0.9477 0.9064 0.9039 0.9108 DuRN 0.9089 0.8834 0.9153 0.9205 0.9487 0.9071 0.9078 0.9131 HDDNet 0.9137 0.8858 0.9117 0.9147 0.9505 0.9081 0.9050 0.9128 Noise level Method Cameraman House Peppers Starfish Butterfly Airplane Parrot Average σ=30 DnCNN 0.8500 0.8518 0.8609 0.8453 0.9025 0.8511 0.8425 0.8577 FDnCNN 0.8593 0.8542 0.8648 0.8463 0.9071 0.8537 0.8463 0.8617 FFDNet 0.8599 0.8542 0.8652 0.8457 0.9073 0.8537 0.8467 0.8618 IRCNN 0.8530 0.8474 0.8561 0.8412 0.8990 0.8475 0.8427 0.8553 DuRN 0.8482 0.8507 0.8634 0.8541 0.9020 0.8494 0.8474 0.8593 HDDNet 0.8611 0.8538 0.8661 0.8502 0.9082 0.8545 0.8455 0.8628 σ=50 DnCNN 0.8077 0.8185 0.8090 0.7722 0.8513 0.7978 0.7952 0.8074 FDnCNN 0.8107 0.8245 0.8137 0.7747 0.8553 0.7986 0.7995 0.8110 FFDNet 0.8138 0.8273 0.8164 0.7750 0.8585 0.7997 0.8004 0.8130 IRCNN 0.8028 0.8159 0.8044 0.7675 0.8454 0.7953 0.7953 0.8038 DuRN 0.7954 0.8211 0.8082 0.7800 0.8451 0.7954 0.7921 0.8053 HDDNet 0.8142 0.8293 0.8144 0.7812 0.8575 0.8012 0.7971 0.8136
Table 4. RMSE values of different methods
View tableTable 4. RMSE values of different methods
Noise level Method Cameraman House Peppers Starfish Butterfly Airplane Parrot Average σ=15 DnCNN 5.99 4.54 5.55 6.31 5.55 6.66 6.49 5.87 FDnCNN 5.99 4.47 5.60 6.36 5.62 6.65 6.52 5.89 FFDNet 6.14 4.51 5.70 6.44 5.76 6.74 6.56 5.98 IRCNN 6.03 4.60 5.57 6.44 5.72 6.66 6.49 5.93 DuRN 6.20 4.68 5.70 6.40 5.74 6.69 6.55 5.99 HDDNet 5.98 4.56 5.57 6.30 5.57 6.64 6.51 5.87 σ=30 DnCNN 8.76 6.19 8.20 9.82 8.62 9.93 9.48 8.72 FDnCNN 8.61 5.98 8.14 9.73 8.61 9.91 9.36 8.62 FFDNet 8.76 6.00 8.19 9.76 8.65 10.00 9.42 8.68 IRCNN 8.76 6.27 8.26 9.99 8.82 9.99 9.46 8.74 DuRN 9.06 6.22 8.51 9.75 8.80 10.05 9.57 8.85 HDDNet 8.61 6.09 8.14 9.52 8.52 9.89 9.41 8.60 σ=50 DnCNN 11.05 8.10 10.94 13.32 11.62 13.04 12.18 11.46 FDnCNN 10.94 7.83 10.87 13.29 11.60 13.07 11.95 11.36 FFDNet 11.08 7.74 10.87 13.26 11.50 13.09 11.97 11.36 IRCNN 11.18 8.15 10.96 13.57 11.85 13.11 12.10 11.56 DuRN 11.51 7.94 11.22 13.21 11.75 12.98 12.28 11.55 HDDNet 10.97 7.74 10.91 12.95 11.44 12.93 12.11 11.29
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Haitao Yin, Hao Deng. Dual Residual Denoising Network Based on Hybrid Attention[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1410002
Paper Information
Category: Image Processing
Received: Oct. 12, 2020
Accepted: Nov. 12, 2020
Published Online: Jun. 30, 2021
The Author Email: Haitao Yin (haitaoyin@njupt.edu.cn)
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