Infrared Technology, Volume. 46, Issue 4, 427(2024)

Single-frame Infrared Image Super-Resolution Reconstruction for Real Scenes

Yifeng SHI... Nan CHEN*, Fang ZHU, Wenbiao MAO, Faming LI, Tianfu WANG, Jiqing ZHANG and Libin YAO |Show fewer author(s)
Author Affiliations
  • [in Chinese]
  • show less
    References(27)

    [1] [1] WANG Z, CHEN J, Hoi S C H. Deep learning for image super-resolution: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 43(10): 3365-3387.

    [2] [2] LI J, PEI Z, ZENG T. From beginner to master: A survey for deep learningbased single-image super-resolution[J]. arXiv preprint arXiv:2109.14335, 2021.

    [3] [3] DONG C, LOY C C, HE K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(2): 295-307.

    [4] [4] 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 Conference on Computer Vision and Pattern Recognition, 2016: 1874-1883.

    [5] [5] 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.

    [6] [6] WANG X, YU K, WU S, et al. Esrgan: Enhanced super-resolution generative adversarial networks[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 63-79.

    [7] [7] SUN C, LV J, LI J, et al. A rapid and accurate infrared image superresolution method based on zoom mechanism[J]. Infrared Physics & Technology, 2018, 88: 228-238.

    [8] [8] Suryanarayana G, TU E, YANG J. Infrared super-resolution imaging using multi-scale saliency and deep wavelet residuals[J]. Infrared Physics & Technology, 2019, 97: 177-186.

    [9] [9] YAO T, LUO Y, HU J, et al. Infrared image super-resolution via discriminative dictionary and deep residual network[J]. Infrared Physics & Technology, 2020, 107: 103314.

    [10] [10] Oz N, Sochen N, Markovich O, et al. Rapid super resolution for infrared imagery[J]. Optics Express, 2020, 28(18): 27196-27209.

    [11] [11] 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.

    [12] [12] LI F, HE X, WEI Z, et al. Multiframe infrared image super-resolution reconstruction using generative adversarial networks[J]. Infrared and Laser Engineering, 2018, 47(2): 26-33.

    [13] [13] WEI Z, LIU Y. Gray image super-resolution reconstruction based on improved RDN method[J]. Infrared and Laser Engineering, 2020, 49(S1): 20200173.

    [14] [14] HU L, WANG Z, CHEN T, et al. An improved SRGAN infrared image super-resolution reconstruction algorithm[J]. Journal of System Simulation, 2021, 33(9): 2109-2118.

    [15] [15] QIU D, JIANG J, HU X, et al. Guided transformer for high-resolution visible image guided infrared image super-resolution[J]. Journal of Image and Graphics, 2023, 28(1): 196-206.

    [16] [16] 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.

    [17] [17] TONG T, LI G, LIU X, et al. Image super-resolution using dense skip connections[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 4799-4807.

    [18] [18] ZHANG K, Liang J, Van Gool L, et al. Designing a practical degradation model for deep blind image super-resolution[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 4791-4800.

    [19] [19] WANG X, XIE L, DONG C, et al. Real-esrgan: Training real-world blind super-resolution with pure synthetic data[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 1905-1914.

    [20] [20] ZHANG W, SHI G, LIU Y, et al. A closer look at blind super-resolution: Degradation models, baselines, and performance upper bounds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 527-536.

    [21] [21] LIANG J, CAO J, SUN G, et al. Swinir: Image restoration using swin transformer[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 1833-1844.

    [22] [22] Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video quality assessment[J]. Electronics Letters, 2008, 44(13): 800-801.

    [23] [23] Hanhart P, Korshunov P, Ebrahimi T. Benchmarking of quality metrics on ultra-high definition video sequences[C]//18th International Conference on Digital Signal Processing (DSP)of IEEE, 2013: 1-8.

    [24] [24] Kundu D, Evans B L. Full-reference visual quality assessment for synthetic images: A subjective study[C]// IEEE International Conference on Image Processing (ICIP), 2015: 2374-2378.

    [25] [25] Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal Processing Letters, 2012, 20(3): 209-212.

    [26] [26] Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 2012, 21(12): 4695-4708.

    [27] [27] Blau Y, Mechrez R, Timofte R, et al. The 2018 PIRM challenge on perceptual image super-resolution[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 334-355.

    Tools

    Get Citation

    Copy Citation Text

    SHI Yifeng, CHEN Nan, ZHU Fang, MAO Wenbiao, LI Faming, WANG Tianfu, ZHANG Jiqing, YAO Libin. Single-frame Infrared Image Super-Resolution Reconstruction for Real Scenes[J]. Infrared Technology, 2024, 46(4): 427

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Dec. 6, 2023

    Accepted: --

    Published Online: Sep. 2, 2024

    The Author Email: Nan CHEN (chennan_kip@163.com)

    DOI:

    CSTR:32186.14.

    Topics