• Acta Optica Sinica
  • Vol. 40, Issue 18, 1810001 (2020)
Ruiming Jia1、*, Tong Li1, Shengjie Liu1, Jiali Cui1, and Fei Yuan2
Author Affiliations
  • 1School of Information Science and Technology, North China University of Technology, Beijing 100144, China
  • 2Digital Content Technology and Media Service Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  • show less
    DOI: 10.3788/AOS202040.1810001 Cite this Article Set citation alerts
    Ruiming Jia, Tong Li, Shengjie Liu, Jiali Cui, Fei Yuan. Infrared Simulation Based on Cascade Multi-Scale Information Fusion Adversarial Network[J]. Acta Optica Sinica, 2020, 40(18): 1810001 Copy Citation Text show less
    References

    [1] Mu C P, Peng M S, Dong Q X et al. Infrared image simulation of ground maneuver target and scene based on OGRE[J]. Applied Mechanics and Materials, 3752, 932-935(2015).

    [2] Ma Y, Tian Y[J]. Modeling method of warship radiation model for infrared simulation Tactical Missile Technology, 2013, 67-70,75.

    [3] Yang M, Li M, Yi Y X et al. Infrared simulation of ship target on the sea based on OGRE[J]. Laser & Infrared, 47, 53-57(2017).

    [4] Xie J R, Li F M, Wei H et al. Infrared target simulation method based on generative adversarial neural networks[J]. Acta Optica Sinica, 39, 0311002(2019).

    [5] Zhou Q, Bai T Z, Liu M Q et al. Near infrared scene simulation based on visual image[J]. Infrared Technology, 37, 11-15(2015).

    [6] Li M, Xu Z W, Xie H W et al. Infrared image generation method and detail modulation based on visible light images[J]. Infrared Technology, 40, 34-38(2018).

    [7] Eigen D, Puhrsch C, Fergus R. Depth map prediction from a single image using a multi-scale deep network. [C]∥International Conference on Neural Information Processing Systems, 2366-2374(2014).

    [8] Wang P, Shen X H, Lin Z et al. Towards unified depth and semantic prediction from a single image[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 7-12 June 2015, Boston, MA, USA., 2800-2809(2015).

    [9] Xu D, Ricci E, Wanli O Y et al. Multi-scale continuous CRFs as sequential deep networks for monocular depth estimation[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 21-26 July 2017, Honolulu, HI, USA., 161-169(2017).

    [10] Xu D, Wang W, Tang H et al. Structured attention guided convolutional neural fields for monocular depth estimation[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18-23 June 2018, Salt Lake City, UT, USA., 3917-3925(2018).

    [11] Laina I, Rupprecht C, Belagiannis V et al. Deeper depth prediction with fully convolutional residual networks[C]∥2016 Fourth International Conference on 3D Vision (3DV). 25-28 Oct. 2016, Stanford, CA, USA., 239-248(2016).

    [12] Jia R M, Liu L Q, Liu S J et al. Single image depth estimation based on encoder-decoder convolution neural network[J]. Journal of Graphics, 40, 718-724(2019).

    [13] Jia R M, Li Y, Li T et al. -, 3428, 0056477(1000).

    [14] Qi XJ, Liao RJ, Liu ZZ, et al.GeoNet: geometric neural network for joint depth and surface normal estimation[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18-23 June 2018, Salt Lake City, UT, USA. New York: IEEE Press, 2018: 283- 291.

    [15] Yin ZC, Shi JP. GeoNet: unsupervised learning of dense depth, optical flow and camera pose[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18-23 June 2018, Salt Lake City, UT, USA. New York: IEEE Press, 2018: 1983- 1992.

    [16] RanjanA, JampaniV, BallesL, et al.Competitive collaboration: joint unsupervised learning of depth, camera motion, optical flow and motion segmentation[C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15-20 June 2019, Long Beach, CA, USA. New York: IEEE Press, 2019: 12232- 12241.

    [17] Jiao J B, Cao Y, Song Y B et al. Look deeper into depth: monocular depth estimation with semantic booster and attention-driven loss[J]. Computer Vision-ECCV, 2018, 53-69(2018).

    [18] Mirza M. -11-06)[2020-04-01]. https: ∥arxiv., org/abs/1411, 1784(2014).

    [19] Hu L M, Zhang Y. Facial image translation in short-wavelength infrared and visible light based on generative adversarial network[J]. Acta Optica Sinica, 40, 0510001(2020).

    [20] Isola P, Zhu J Y, Zhou T H et al. Image-to-image translation with conditional adversarial networks[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 21-26 July 2017, Honolulu, HI, USA., 5967-5976(2017).

    [21] Ma S, Fu J L, Chen C W et al. DA-, 5657-5666(2018).

    [22] Mejjati Y A, Richardt C, Tompkin J et al. -11-08)[2020-04-01]. https: ∥arxiv., org/abs/1806, 02311(2018).

    [23] Tang H, Xu D, Sebe N et al. Multi-channel attention selection GAN with cascaded semantic guidance for cross-view image translation[C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15-20 June 2019, L, 2412-2421(2019).

    [24] Luo W J, Li Y J, Urtasun R et al. -01-25)[2020-04-01]. https: ∥arxiv., org/abs/1701, 04128(2017).

    [25] HwangS, ParkJ, KimN, et al.Multispectral pedestrian detection: Benchmark dataset and baseline[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 7-12 June 2015, Boston, MA, USA. New York: IEEE Press, 2015: 1037- 1045.

    [26] Ioffe S. -03-02)[2020-04-01]. https: ∥arxiv., org/abs/1502, 03167(2015).

    [27] Jia R M, Qiu Z Z, Cui J L et al. Deep multi-scale encoder-decoder convolutional network for blind deblurring[J]. Journal of Computer Applications, 39, 2552-2557(2019).

    [28] Lin GS, MilanA, Shen CH, et al.RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 21-26 July 2017, Honolulu, HI, USA. New York: IEEE Press, 2017: 5168- 5177.

    Ruiming Jia, Tong Li, Shengjie Liu, Jiali Cui, Fei Yuan. Infrared Simulation Based on Cascade Multi-Scale Information Fusion Adversarial Network[J]. Acta Optica Sinica, 2020, 40(18): 1810001
    Download Citation