Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (3): 922-930.DOI: 10.11772/j.issn.1001-9081.2023030367
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Rui JIANG1,2, Wei LIU1,2(), Cheng CHEN1,2, Tao LU1,2
Received:
2023-04-07
Revised:
2023-06-08
Accepted:
2023-06-13
Online:
2023-09-07
Published:
2024-03-10
Contact:
Wei LIU
About author:
JIANG Rui, born in 1998, M. S. candidate. His research interests include image processing, deep learning.Supported by:
通讯作者:
刘威
作者简介:
江锐(1998—),男,湖北黄冈人,硕士研究生,主要研究方向:图像处理、深度学习基金资助:
CLC Number:
Rui JIANG, Wei LIU, Cheng CHEN, Tao LU. Asymmetric unsupervised end-to-end image deraining network[J]. Journal of Computer Applications, 2024, 44(3): 922-930.
江锐, 刘威, 陈成, 卢涛. 非对称端到端的无监督图像去雨网络[J]. 《计算机应用》唯一官方网站, 2024, 44(3): 922-930.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023030367
模型 | HeavyRain | Rain800 | Rain1200 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | LPIPS | PSNR/dB | SSIM | LPIPS | PSNR/dB | SSIM | LPIPS | ||
有监督去雨模型 | Detail | 16.40 | 0.501 | 0.709 | 22.00 | 0.780 | 0.131 | 21.53 | 0.798 | 0.091 |
LPNet | 15.88 | 0.512 | 0.602 | 20.92 | 0.756 | 0.188 | 22.22 | 0.781 | 0.127 | |
SPANet | 16.65 | 0.506 | 0.572 | 22.68 | 0.787 | 0.205 | 23.53 | 0.770 | 0.159 | |
DualGCN | 15.31 | 0.655 | 0.420 | 22.22 | 0.782 | 0.107 | 27.34 | 0.863 | 0.076 | |
半监督去雨模型 | SEMI | 16.45 | 0.398 | 0.607 | 21.16 | 0.731 | 0.138 | 22.50 | 0.724 | 0.185 |
无监督去雨模型 | CycleDerain | 18.64 | 0.532 | 0.359 | 21.75 | 0.721 | 0.210 | 21.30 | 0.694 | 0.308 |
本文方法 | 24.52 | 0.821 | 0.062 | 23.19 | 0.789 | 0.104 | 25.73 | 0.849 | 0.070 |
Tab. 1 Objective evaluation indexes of deraining effects by different methods on synthetic rain datasets
模型 | HeavyRain | Rain800 | Rain1200 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | LPIPS | PSNR/dB | SSIM | LPIPS | PSNR/dB | SSIM | LPIPS | ||
有监督去雨模型 | Detail | 16.40 | 0.501 | 0.709 | 22.00 | 0.780 | 0.131 | 21.53 | 0.798 | 0.091 |
LPNet | 15.88 | 0.512 | 0.602 | 20.92 | 0.756 | 0.188 | 22.22 | 0.781 | 0.127 | |
SPANet | 16.65 | 0.506 | 0.572 | 22.68 | 0.787 | 0.205 | 23.53 | 0.770 | 0.159 | |
DualGCN | 15.31 | 0.655 | 0.420 | 22.22 | 0.782 | 0.107 | 27.34 | 0.863 | 0.076 | |
半监督去雨模型 | SEMI | 16.45 | 0.398 | 0.607 | 21.16 | 0.731 | 0.138 | 22.50 | 0.724 | 0.185 |
无监督去雨模型 | CycleDerain | 18.64 | 0.532 | 0.359 | 21.75 | 0.721 | 0.210 | 21.30 | 0.694 | 0.308 |
本文方法 | 24.52 | 0.821 | 0.062 | 23.19 | 0.789 | 0.104 | 25.73 | 0.849 | 0.070 |
方法 | Real147 | RID | ||
---|---|---|---|---|
NIQE | SSEQ | NIQE | SSEQ | |
Detail | 4.276 | 28.607 | 4.326 | 27.535 |
LPNet | 4.831 | 31.509 | 4.797 | 31.265 |
SPANet | 3.987 | 30.214 | 3.591 | 27.969 |
DualGCN | 3.801 | 29.801 | 3.486 | 27.126 |
SEMI | 3.751 | 30.423 | 3.524 | 30.506 |
CycleDerain | 4.361 | 34.146 | 4.560 | 38.009 |
本文方法 | 3.723 | 28.481 | 3.432 | 28.949 |
Tab. 2 Objective evaluation indexes of deraining effects ofdifferent methods on real rain images
方法 | Real147 | RID | ||
---|---|---|---|---|
NIQE | SSEQ | NIQE | SSEQ | |
Detail | 4.276 | 28.607 | 4.326 | 27.535 |
LPNet | 4.831 | 31.509 | 4.797 | 31.265 |
SPANet | 3.987 | 30.214 | 3.591 | 27.969 |
DualGCN | 3.801 | 29.801 | 3.486 | 27.126 |
SEMI | 3.751 | 30.423 | 3.524 | 30.506 |
CycleDerain | 4.361 | 34.146 | 4.560 | 38.009 |
本文方法 | 3.723 | 28.481 | 3.432 | 28.949 |
模型 | 不同图像的NIQE | |
---|---|---|
图像1 | 图像2 | |
模型A | 3.501 | 4.502 |
模型B | 3.763 | 4.412 |
模型C | 3.932 | 4.425 |
模型D | 3.279 | 4.031 |
本文模型 | 3.268 | 4.016 |
Tab. 3 Objective evaluation indexes of ablation experiments
模型 | 不同图像的NIQE | |
---|---|---|
图像1 | 图像2 | |
模型A | 3.501 | 4.502 |
模型B | 3.763 | 4.412 |
模型C | 3.932 | 4.425 |
模型D | 3.279 | 4.031 |
本文模型 | 3.268 | 4.016 |
方法 | 图像1 | 图像2 | 图像3 | 图像4 | ||||
---|---|---|---|---|---|---|---|---|
NIQE | SSEQ | NIQE | SSEQ | NIQE | SSEQ | NIQE | SSEQ | |
DCPDN | 2.387 | 19.706 | 3.828 | 10.234 | 5.013 | 24.140 | 3.968 | 21.477 |
DehazeNet | 2.897 | 8.608 | 3.247 | 22.409 | 4.832 | 16.333 | 3.790 | 50.192 |
GCANet | 2.456 | 11.689 | 3.619 | 24.886 | 4.456 | 26.376 | 3.323 | 33.272 |
IDE | 2.203 | 4.844 | 3.003 | 14.056 | 3.347 | 27.881 | 3.661 | 52.372 |
本文方法 | 2.143 | 2.677 | 2.788 | 7.901 | 3.081 | 16.222 | 2.982 | 17.940 |
Tab. 4 Objective evaluation of defogging results for Fig. 9 by different methods
方法 | 图像1 | 图像2 | 图像3 | 图像4 | ||||
---|---|---|---|---|---|---|---|---|
NIQE | SSEQ | NIQE | SSEQ | NIQE | SSEQ | NIQE | SSEQ | |
DCPDN | 2.387 | 19.706 | 3.828 | 10.234 | 5.013 | 24.140 | 3.968 | 21.477 |
DehazeNet | 2.897 | 8.608 | 3.247 | 22.409 | 4.832 | 16.333 | 3.790 | 50.192 |
GCANet | 2.456 | 11.689 | 3.619 | 24.886 | 4.456 | 26.376 | 3.323 | 33.272 |
IDE | 2.203 | 4.844 | 3.003 | 14.056 | 3.347 | 27.881 | 3.661 | 52.372 |
本文方法 | 2.143 | 2.677 | 2.788 | 7.901 | 3.081 | 16.222 | 2.982 | 17.940 |
1 | 李超, 黄新宇, 王凯. 基于特征融合和自学习锚框的高分辨率图像小目标检测算法[J]. 电子学报, 2022, 50(7): 1684-1695. 10.12263/DZXB.20200917 |
LI C, HUANG X Y, WANG K. Small target detection of high-resolution images based on feature fusion and learnable anchor [J]. Acta Electronica Sinica, 2022, 50(7): 1684-1695. 10.12263/DZXB.20200917 | |
2 | 任莎莎, 刘琼. 小目标特征增强图像分割算法[J]. 电子学报, 2022, 50(8):1894-1904. 10.12263/DZXB.20211123 |
REN S S, LIU Q. A tiny target feature enhancement algorithm for image segmentation [J]. Acta Electronica Sinica, 2022, 50(8): 1894-1904. 10.12263/DZXB.20211123 | |
3 | KANG L-W, LIN C-W, FU Y-H. Automatic single-image-based rain streaks removal via image decomposition [J]. IEEE Transactions on Image Processing, 2012, 21(4): 1742-1755. 10.1109/tip.2011.2179057 |
4 | LUO Y, XU Y, JI H. Removing rain from a single image via discriminative sparse coding [C]// Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2015: 3397-3405. 10.1109/iccv.2015.388 |
5 | ZHU L, FU C-W, LISCHINSKI D, et al. Joint bi-layer optimization for single-image rain streak removal [C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2545-2553. 10.1109/iccv.2017.276 |
6 | LI Y, TAN R T, GUO X, et al. Rain streak removal using layer priors[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 2736-2744. 10.1109/cvpr.2016.299 |
7 | FU X, HUANG J, DING X, et al. Clearing the skies: a deep network architecture for single-image rain removal [J]. IEEE Transactions on Image Processing, 2017, 26(6): 2944-2956. 10.1109/tip.2017.2691802 |
8 | REN D, ZUO W, HU Q, et al. Progressive image deraining networks: a better and simpler baseline [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 3937-3946. 10.1109/cvpr.2019.00406 |
9 | ZHANG H, SINDAGI V, PATEL V M. Image de-raining using a conditional generative adversarial network [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 30(11): 3943-3956. 10.1109/tcsvt.2019.2920407 |
10 | YANG W, TAN R T, FENG J, et al. Deep joint rain detection and removal from a single image [C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 1357-1366. 10.1109/cvpr.2017.183 |
11 | LI R, L-F CHEONG, TAN R T. Heavy rain image restoration: integrating physics model and conditional adversarial learning [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 1633-1642. 10.1109/cvpr.2019.00173 |
12 | 盖杉, 王俊生. 基于深度学习的非局部注意力增强网络图像去雨算法研究[J]. 电子学报, 2020, 48(10): 1899-1908. 10.3969/j.issn.0372-2112.2020.10.004 |
GAI S, WANG J S. Image raindrop algorithm research using nonlocal attention enhanced network based on deep learning [J]. Acta Electronica Sinica, 2020, 48(10): 1899-1908. 10.3969/j.issn.0372-2112.2020.10.004 | |
13 | GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets [C]// Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2014: 2672-2680. |
14 | ZHU H, PENG X, ZHOU J T, et al. Singe image rain removal with unpaired information: A differentiable programming perspective[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 9332-9339. 10.1609/aaai.v33i01.33019332 |
15 | JIN X, CHEN Z, LIN J, et al. Unsupervised single image deraining with self-supervised constraints [C]// Proceedings of the 2019 IEEE International Conference on Image Processing. Piscataway: IEEE, 2019: 2761-2765. 10.1109/icip.2019.8803238 |
16 | WEI Y, ZHANG Z, WANG Y, et al. DerainCycleGAN: rain attentive CycleGAN for single image deraining and rainmaking [J]. IEEE Transactions on Image Processing, 2021, 30: 4788-4801. 10.1109/tip.2021.3074804 |
17 | ZHU J-Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks [C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2223-2232. 10.1109/iccv.2017.244 |
18 | HU J, SHEN L, SUN G. Squeeze-and-excitation networks [C]// Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 7132-7141. 10.1109/cvpr.2018.00745 |
19 | WOO S, PARK J, LEE J-Y, et al. CBAM: convolutional block attention module [C]// Proceedings of the 2018 European Conference on Computer Vision. Cham: Springer, 2018: 3-19. 10.1007/978-3-030-01234-2_1 |
20 | WANG T, YANG X, XU K, et al. Spatial attentive single-image deraining with a high quality real rain dataset [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 12270-12279. 10.1109/cvpr.2019.01255 |
21 | WANG C, WU Y, SU Z, et al. Joint self-attention and scale-aggregation for self-calibrated deraining network [C]// Proceedings of the 28th ACM International Conference on Multimedia. New York: ACM, 2020: 2517-2525. 10.1145/3394171.3413559 |
22 | ZHANG H, PATEL V M. Density-aware single image de-raining using a multi-stream dense network [C]// Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 695-704. 10.1109/cvpr.2018.00079 |
23 | WEI W, MENG D, ZHAO Q, et al. Semi-supervised transfer learning for image rain removal [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 3877-3886. 10.1109/cvpr.2019.00400 |
24 | LI S, ARAUJO I B, REN W, et al. Single image deraining: a comprehensive benchmark analysis [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 3838-3847. 10.1109/cvpr.2019.00396 |
25 | ZHANG Y, DING L, SHARMA G. HazeRD: an outdoor scene dataset and benchmark for single image dehazing [C]// Proceedings of the 2017 IEEE International Conference on Image Processing. Piscataway: IEEE, 2017: 3205-3209. 10.1109/icip.2017.8296874 |
26 | FU X, HUANG J, ZENG D, et al. Removing rain from single images via a deep detail network [C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 3855-3863. 10.1109/cvpr.2017.186 |
27 | FU X, LIANG B, HUANG Y, et al. Lightweight pyramid networks for image deraining [J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(6): 1794-1807. 10.1109/tnnls.2019.2926481 |
28 | FU X, QI Q, ZHA Z-J, et al. Rain streak removal via dual graph convolutional network [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(2): 1352-1360. 10.1609/aaai.v35i2.16224 |
29 | HUYNH-THU Q, GHANBARI M. Scope of validity of PSNR in image/video quality assessment [J]. Electronics Letters, 2008, 44(13): 800-801. 10.1049/el:20080522 |
30 | WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. 10.1109/tip.2003.819861 |
31 | ZHANG R, ISOLA P, EFROS A A, et al. The unreasonable effectiveness of deep features as a perceptual metric [C]// Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 586-595. 10.1109/cvpr.2018.00068 |
32 | MITTAL A, SOUNDARARAJAN R, BOVIK A C. Making a “completely blind” image quality analyzer [J]. IEEE Signal Processing Letters, 2012, 20(3): 209-212. 10.1109/lsp.2012.2227726 |
33 | LIU L, LIU B, HUANG H, et al. No-reference image quality assessment based on spatial and spectral entropies[J]. Signal Processing: Image Communication, 2014, 29(8): 856-863. 10.1016/j.image.2014.06.006 |
34 | ZHANG H, PATEL V M. Densely connected pyramid dehazing network [C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 3194-3203. 10.1109/cvpr.2018.00337 |
35 | CAI B, XU X, JIA K, et al. DehazeNet: an end-to-end system for single image haze removal [J]. IEEE Transactions on Image Processing, 2016, 25(11): 5187-5198. 10.1109/tip.2016.2598681 |
36 | CHEN D, HE M, FAN Q, et al. Gated context aggregation network for image dehazing and deraining [C]// Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2019: 1375-1383. 10.1109/wacv.2019.00151 |
37 | JU M, DING C, REN W, et al. IDE: image dehazing and exposure using an enhanced atmospheric scattering model [J]. IEEE Transactions on Image Processing, 2021, 30: 2180-2192. 10.1109/tip.2021.3050643 |
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