[1]曹锦纲,李金华,郑顾平.基于生成式对抗网络的道路交通模糊图像增强[J].智能系统学报,2020,15(3):491-498.[doi:10.11992/tis.201903041]
 CAO Jin gang,LI Jinhua,ZHENG Guping.Enhancement of blurred road-traffic images based on generative adversarial network[J].CAAI Transactions on Intelligent Systems,2020,15(3):491-498.[doi:10.11992/tis.201903041]
点击复制

基于生成式对抗网络的道路交通模糊图像增强(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年3期
页码:
491-498
栏目:
学术论文—智能系统
出版日期:
2020-05-05

文章信息/Info

Title:
Enhancement of blurred road-traffic images based on generative adversarial network
作者:
曹锦纲 李金华 郑顾平
华北电力大学 控制与计算机工程学院,河北 保定 071003
Author(s):
CAO Jin’ gang LI Jinhua ZHENG Guping
School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
关键词:
图像增强道路交通运动模糊多尺度多权重残差网络神经网络生成式对抗网络
Keywords:
enhancementroad trafficmotion blurmulti-scalemulti-weightresidual networkneural networkgenerated adversarial network
分类号:
TP391
DOI:
10.11992/tis.201903041
摘要:
为了提高道路交通模糊图像增强的质量,进一步促进道路交通管理,针对道路交通场景下的运动模糊图像增强问题,提出了一种基于生成式对抗网络的多尺度多路径学习的模型。首先,选用具有多尺度卷积核的神经网络,对输入的图像进行更细致地特征值提取;其次,将局部残差学习和全局残差学习相结合,采用多路径多权重共享的递归学习,并利用判别网络和生成网络间的对抗训练优化网络参数;最后,实现端到端直接生成图像。实验结果表明:提出的模型可以有效地增强道路交通场景下的运动模糊图像,生成的图像细节更加丰富,具有较好的图像视觉效果。
Abstract:
To improve the quality of blurred road-traffic images and facilitate road traffic management, we propose a multi-scale multi-path learning model based on a generative adversarial network, which solves the problem of enhancing motion-blur images in road traffic scenarios. First, the model selects a neural network with a multi-scale convolution kernel to extract the eigenvalues of the input image in more detail. Then, by combining local and global residual learning techniques and applying recursive learning with multi-path and multi-weight sharing, the model performs adversarial training between discriminant and generating networks to optimize the network parameters. Lastly, an image is generated directly end to end. The experimental results show that the proposed model can effectively enhance motion-blur images in road traffic scenarios, and the details of the generated image are richer and have better visual effects.

参考文献/References:

[1] 陈春雷, 叶东毅, 陈昭炯. 多局部模糊核融合的图像盲去模糊算法[J]. 光子学报, 2018, 47(10): 205-215
CHEN Chunlei, YE Dongyi, CHEN Zhaojiong. Blind image deblurring via multi-local kernels’ fusion[J]. Acta photonica sinica, 2018, 47(10): 205-215
[2] BAHAT Y, EFRAT N, IRANI M. Non-uniform blind deblurring by reblurring[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy, 2017: 3306-3314.
[3] CHAN T F, WONG C K. Total variation blind deconvolution[J]. IEEE transactions on image processing, 1998, 7(3): 370-375.
[4] CHO S, LEE S. Fast motion deblurring[J]. ACM transactions on graphics, 2009, 28(5): 1-8.
[5] XU Li, JIA Jiaya. Two-phase kernel estimation for robust motion deblurring[C]//Proceedings of the 11th European Conference on Computer Vision. Crete, Greece, 2010: 157-170.
[6] XU Li, ZHENG Shicheng, JIA Jiaya. Unnatural l0 sparse representation for natural image deblurring[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 1107-1114.
[7] GOLDSTEIN A, FATTAL R. Blur-kernel estimation from spectral irregularities[C]//Proceedings of the 12th European Conference on Computer Vision. Florence, Italy, 2012: 622-635.
[8] PAN Jinshan, HU Zhe, SU Zhixun, et al. Deblurring text images via l0-regularized intensity and gradient prior[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014: 2901-2908.
[9] PAN Jinshan, SUN Deqing, PFISTER H, et al. Blind image deblurring using dark channel prior[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 1628-1636.
[10] SCHULER C J, HIRSCH M, HARMELING S, et al. Learning to deblur[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 38(7): 1439-1451.
[11] SUN Jian, CAO Wenfei, XU Zongben, et al. Learning a convolutional neural network for non-uniform motion blur removal[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015: 769-777.
[12] XIAO Lei, WANG Jue, HEIDRICH W, et al. Learning high-order filters for efficient blind deconvolution of document photographs[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2016: 734-749.
[13] NAH S, KIM T H, LEE K M. Deep multi-scale convolutional neural network for dynamic scene deblurring[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017: 257-265.
[14] KUPYN O, BUDZAN V, MYKHAILYCH M, et al. DeblurGAN: blind motion deblurring using conditional adversarial networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 8183-8192.
[15] MIRZA M, OSINDERO S. Conditional generative adversarial nets[J/OL]. [2019 –03 –29].https: //arxiv.org/abs/1411.1784.
[16] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge, USA, 2014: 2672-2680.
[17] GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of Wasserstein GANs[C]//Proceedings of 31st Annual Conference on Neural Information Processing Systems. Long Beach, USA, 2017: 5769-5779.
[18] JOHNSON J, ALAHI A, LI Feifei. Perceptual losses for real-time style transfer and super-resolution[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2016: 694-711.
[19] KIM T H, LEE K M, SCH?LKOPF B, et al. Online video deblurring via dynamic temporal blending network[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy, 2017: 4058-4067.
[20] SU Shuochen, DELBRACIO M, WANG Jue, et al. Deep video deblurring for hand-held cameras[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017: 237-246.
[21] ISOLA P, ZHU Junyan, ZHOU Tinghui, et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017: 5967-5976.

相似文献/References:

[1]谭营,王军.手指静脉身份识别技术最新进展[J].智能系统学报,2011,6(06):471.
 TAN Ying,WANG Jun.Recent advances in finger vein based biometric techniques[J].CAAI Transactions on Intelligent Systems,2011,6(3):471.
[2]谌琛,李卫军,陈亮,等.一种自适应的仿生图像增强方法:LDRF算法[J].智能系统学报,2012,7(05):404.
 CHEN Chen,LI Weijun,CHEN Liang,et al.An adaptive biomimetic image processing method: LDRF algorithm[J].CAAI Transactions on Intelligent Systems,2012,7(3):404.

备注/Memo

备注/Memo:
收稿日期:2019-03-29。
基金项目:中央高校基本科研业务费专项资金资助项目(2018MS072)
作者简介:曹锦纲,博士研究生,主要研究方向为图像处理和模式识别。发表学术论文10余篇;李金华,硕士研究生,主要研究方向为图像处理和人工智能;郑顾平,教授,博士,主要研究方向为图像处理、人工智能、大数据分析。发表学术论文50余篇
通讯作者:李金华.E-mail:1844623784@qq.com
更新日期/Last Update: 1900-01-01