• Infrared and Laser Engineering
  • Vol. 51, Issue 6, 20210605 (2022)
Xiangjun Wang1、2 and Wensen Ouyang1、2
Author Affiliations
  • 1State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
  • 2MOEMS Education Ministry Key Laboratory, Tianjin University, Tianjin 300072, China
  • show less
    DOI: 10.3788/IRLA20210605 Cite this Article
    Xiangjun Wang, Wensen Ouyang. Multi-scale recurrent attention network for image motion deblurring[J]. Infrared and Laser Engineering, 2022, 51(6): 20210605 Copy Citation Text show less
    References

    [1] Anti Li, Dingjie Wu, Chenglong Li. Survey of autonomous collision avoidance algorithm for unmanned aerial vehicle in low altitude. Electronics Optics & Control, 28, 1-8(2021).

    [2] Feng Lu, Huahai Liu, Changying Huang, . Overview of target detection techniques based on deep learning. Computer Systems Applications, 30, 1-13(2021).

    [3] Wen Gao, Ming Zhu, Baigen He, . Overview of target tracking technology. Chinese Optics, 7, 365-375(2013).

    [4] Krishnan D, Tay T, Fergus R, et al. Blind deconvolution using a nmalized sparsity measure[C]Proceedings of 2011 IEEE International Conference on Computer Vision Pattern Recognition, 2011: 233240.

    [5] Pan J, Sun D, Pfister H, et al. Blind image deblurring using dark channel pri[C]Proceedings of 2016 IEEE International Conference on Computer Vision Pattern Recognition, 2016: 16281636.

    [6] O Whyte, J Sivic, A Zisserman, et al. Non-uniform deblurring for shaken images. International Journal of Computer Vision, 98, 168-186(2012).

    [7] Luoyu Zhou, Bao Zhang, Yang Yang. Estimation of parameter of defocused blurred image using Hough transform. Infrared and Laser Engineering, 41, 2833-2837(2012).

    [8] S Nah, T H Kim, K M Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. IEEE Computer Vision and Pattern Recognition, 35, 257-265(2017).

    [9] Kupyn O, Budzan V, Mykhailych M, et al. DeblurGAN: blind motion deblurring using conditional adversarial wks[C]2018 IEEECVF Conference on Computer Vision Pattern Recognition, 2018: 81838192.

    [10] Tao X, Gao H, Wang Y, et al. Scalerecurrent wk f deep image deblurring[C]2018 IEEECVF Conference on Computer Vision Pattern Recognition. IEEE, 2018.

    [11] Kupyn O, Martyniuk T, Wu J, et al. DeblurGANv2: Deblurring (dersofMagnitude) Faster Better[C]2019 IEEECVF International Conference on Computer Vision (ICCV). IEEE, 2019.

    [12] Zhang K, Luo W, Zhong Y, et al. Deblurring by realistic blurring[C]2020 IEEECVF Conference on Computer Vision Pattern Recognition (CVPR). IEEE, 2020.

    [13] Q Qi, J Guo, W Jin. Attention network for non-uniform deblurring. IEEE Access, 8, 100044-100057(2020).

    [14] Howard A G, Zhu M, Chen B, et al. Mobiles: Efficient convolutional neural wks f mobile vision applications[J]. Computer Vision Pattern Recognition, 2017: 10.48550arXiv.1704.04861.

    [15] Woo S, Park J, Lee J Y, et al. CBAM:Convolutional block attention module[C]European Conference on Computer Vision, 2018: arXiv:1807.06521.

    [16] Qian P, Wu Y, Zhang X. Dense connected residual generative adversarial wk f single image deblurring[C]2021 IEEE 5th Advanced Infmation Technology, Electronic Automation Control Conference (IAEAC), 2021: 461466.

    [17] Lai W S, Huang J B, Hu Z, et al. A comparative studyf single image blind deblurring [C]Computer Vision Pattern Recognition, IEEE, 2016: 17011709.

    [18] Köhler R, Hirsch M, Mohler B, et al. Recding playback of camera shake: benchmarking blind deconvolution with a realwld database [C]Conferenceon Computer VisionECCV, 2012: 2740.

    CLP Journals

    [1] Zhongxiang Pang, Xie Liu, Guihua Liu, Yinjun Gong, Han Zhou, Hongwei Luo. Parallel multifeature extracting network for infrared image enhancement[J]. Infrared and Laser Engineering, 2022, 51(8): 20210957

    Xiangjun Wang, Wensen Ouyang. Multi-scale recurrent attention network for image motion deblurring[J]. Infrared and Laser Engineering, 2022, 51(6): 20210605
    Download Citation