[1]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]
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Enhancement of blurred road-traffic images based on generative adversarial network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 3 Page number: 491-498 Column: 学术论文—智能系统 Public date: 2020-05-05
Title:
Enhancement of blurred road-traffic images based on generative adversarial network
Author(s):
CAO Jin’ gang;  LI Jinhua;  ZHENG Guping
School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
enhancement; road traffic; motion blur; multi-scale; multi-weight; residual network; neural network; generated adversarial network
CLC:
TP391
DOI:
10.11992/tis.201903041
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.
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.
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