Infrared Technology, Volume. 45, Issue 5, 498(2023)

Infrared Images with Super-resolution Based on Deep Convolutional Neural Network

[in Chinese]1, [in Chinese]1、*, [in Chinese]1, [in Chinese]1, [in Chinese]1, [in Chinese]1, and [in Chinese]2
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    References(18)

    [1] [1] Baker S, Kanade T. Limits on super resolution and how to break them[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 24(9): 1167-1183.

    [2] [2] Hou H, Andrews H. Cubic splines for image interpolation and digital filtering[J]. IEEE Transactions on acoustics, speech, and signal processing, 1978, 26(6): 508-517.

    [3] [3] Freeman W T, Pasztor E C, Carmichael O T. Learning low-level vision[J]. International Journal of Computer Vision, 2000, 40(1): 25-47.

    [4] [4] YANG J, Wright J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE transactions on image processing, 2010, 19(11): 2861-2873.

    [5] [5] DONG C, Loy C C, HE K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Trans Pattern Anal Mach Intell, 2016, 38(2): 295-307.

    [6] [6] DONG C, Loy C C, TANG X. Accelerating the super-resolution convolutional neural network[C]//Proceedings of the European conference on computer vision (ECCV), 2016: 391-407.

    [7] [7] SHI W, Caballero J, Huszár F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2016: 1874-1883.

    [8] [8] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016: 770-778.

    [9] [9] Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4681-4690.

    [10] [10] Lim B, Son S, Kim H, et al. Enhanced deep residual networks for single image super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops,2017: 136-144.

    [11] [11] ZHANG Y, LI K, LI K, et al. Image super-resolution using very deep residual channel attention networks[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 286-301.

    [12] [12] DAI T, CAI J, ZHANG Y, et al. Second-order attention network for single image super-resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 11065-11074.

    [13] [13] NIU B, WEN W, REN W, et al. Single image super-resolution via a holistic attention network[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2020: 191-207.

    [14] [14] Choi Y, Kim N, Hwang S, et al. Thermal image enhancement using convolutional neural network[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2016: 223-230.

    [15] [15] HE Z, TANG S, YANG J, et al. Cascaded deep networks with multiple receptive fields for infrared image super-resolution[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 29(8): 2310-2322.

    [16] [16] ZOU Y, ZHANG L, LIU C, et al. Super-resolution reconstruction of infrared images based on a convolutional neural network with skip connections[J]. Optics and Lasers in Engineering, 2021, 146: 106717.

    [17] [17] YU J, FAN Y, YANG J, et al. Wide activation for efficient and accurate image super-resolution[J/OL]. arXiv preprint arXiv:1808.08718, 2018.

    [18] [18] WANG Q, WU B, ZHU P, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 11534-11542.

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    [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Infrared Images with Super-resolution Based on Deep Convolutional Neural Network[J]. Infrared Technology, 2023, 45(5): 498

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    Paper Information

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    Received: Dec. 30, 2022

    Accepted: --

    Published Online: Jan. 15, 2024

    The Author Email: (zbhmatt@163.com)

    DOI:

    CSTR:32186.14.

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