• Infrared and Laser Engineering
  • Vol. 47, Issue 2, 203003 (2018)
Li Fangbiao1、2、*, He Xin1, Wei Zhonghui1, He Jiawei1, and He Dinglong1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    DOI: 10.3788/irla201847.0203003 Cite this Article
    Li Fangbiao, He Xin, Wei Zhonghui, He Jiawei, He Dinglong. Multiframe infrared image super-resolution reconstruction using generative adversarial networks[J]. Infrared and Laser Engineering, 2018, 47(2): 203003 Copy Citation Text show less
    References

    [1] Chandra M S, Rajan K, Srinivasan R. Locally adaptive regularization for robust multiframe super resolution reconstruction[J]. Advances in Computer Science, Eng & Appl, 2012, AISC166: 223-234.

    [2] Xiao C, Yu J, Xue Y. A high-efficiency super-resolution reconstruction algorithm from image/video sequences[C]//Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, 2008: 573-581.

    [3] Luo W, Zhang Y, Feizi A, et al. Pixel super-resolution using wavelength scanning[J]. Light: Science & Application, 2016, 5(4): e16060.

    [4] Zhaoghao K, Chen L, Yang X S, et al. Super-resolution dipole orientation mapping via polarization demodulation[J]. Light: Science & Application, 2016, 5(10): e16166.

    [5] Ashikaga H, Estner H L, Herzka D A, et al. Quantitative assessment of single-image super-resolution in myocardical scar imaging[J]. Medical Imaging and Diagnostic Radiology, 2014, 2(1): 1-12.

    [6] Lei Sen, Shi Zhenwei, Zou Zhengxia. Super-resolution for remote sensing images via local-global combined network[J].IEEE Geosciences and Remote Sensing Letters, 2017, 14(8): 1243-1247.

    [7] Pan Z, Yu J, Huang H, et al. Super-resolution based on compressive sensing and structural self-similarity for remote sensing images[J]. IEEE Transactions Geosci and Remote Sensing Letters, 2013, 51(9): 4864-4876.

    [8] Zhou F, Yang W M, Liao Q M. Interpolation-based image super-resolution using multisurface fitting[J]. IEEE Transactions on Image Processing, 2012, 21(7): 3312-3318.

    [9] Freedman G, Fattal R. Image and video upscaling from local self-examples[J]. TOG, 2011, 28(2): 1-11.

    [10] Liu Xueting, Song Daojin, Dong Chuandai, et al. MAP-based image superresolution reconstructionscience[J]. International Journal of Computer Science Engineering, 2008, 2(1): dai: 10.1999/1307-6892/2034.

    [11] Simon Baker, Takeo Kanad. Limits on super-resolution and how to break them[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(9): 1167-1183.

    [12] Marcia L S A, Neison D A. Mascarenhas, generalization of iterative restoration techniques for super-resolution[C]// SIBGRAPI Conference on Graphics, Patterns and Images, 2011: 258-265.

    [13] Yang J C, Wright J, Huang T S, et al. Image super-resolution via sparse representation[J]. IEEE Signal Processing Magazine, 2010, 19(11): 2861-2873.

    [14] Timofte R, Smet V D, Gool L V. Anchored neighborhood regression for fast example-based super-resolution[C]// IEEE International Conference on Computer Vision, 2013: 1920-1927.

    [15] Wang Xinwei, Cao Yinan, Lu Dezhen, et al. Spatial difference shaping to improve range resolution in 3D super-resolution range-gated imaging[C]//2015 Inernational Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 2015, 9622: 1-8.

    [16] Zhang Di, He Jiazhong. Hybrid sparse-representation-based approach to image super-resolution reconstruction[J]. Journal of Electronic Imaging, 2017, 26(2): 02308.

    [17] Dong Chao, Chen Change Loy, He Kaiming, et al. Image super-resolution using deep convolutianal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295-307.

    [18] Kappeler A, Yoo Seunghwan, Dai Qiqin, et al. Video super-resolution with convolutional neural networks[J]. IEEE Transactions on Computational Imaging, 2016, 2(2): 109-122.

    [19] Shi Wenzhe, Caballero J, Huszar F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2016: 1874-1883.

    [20] Caballero J, Ledig C, Aitken A, et al. Real-time video super-resolution with spatio-temporal networks and motion compensation[C]//Computer Vision and Pattern Recognition, 2017, arXiv preprint arXiv: 1611. 05250v2.

    [21] Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Computer Vision and Pattern Recognition, 2016, arXiv preprint arXiv: 1609. 04802v1.

    [22] Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks[C]//Machine Learning, 2014, arXiv preprint arXiv: 1406. 2661vl.

    Li Fangbiao, He Xin, Wei Zhonghui, He Jiawei, He Dinglong. Multiframe infrared image super-resolution reconstruction using generative adversarial networks[J]. Infrared and Laser Engineering, 2018, 47(2): 203003
    Download Citation