• Electro-Optic Technology Application
  • Vol. 38, Issue 5, 66 (2023)
ZHAO Yanling1, ZHANG Jing2, and FENG Yingbin1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: Cite this Article
    ZHAO Yanling, ZHANG Jing, FENG Yingbin. Improved Underwater Video Super-resolution Reconstruction Based on BasicVSR[J]. Electro-Optic Technology Application, 2023, 38(5): 66 Copy Citation Text show less
    References

    [4] CHEN Q, ZHANG Z, LI G. Underwater image enhancement based on color balance and multi-scale fusion[J]. IEEE Photonics Journal, 2022, 14(6): 1-10.

    [5] PAULL C K, TALLING P J, MAIER K L, et al. Powerful turbidity currents driven by dense basal layers[J]. Nature Communications, 2018, 9(1): 1-9.

    [6] SHI W, CABALLERO J, FERENC H, 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). Las Vegas, NV, USA: IEEE, 2016: 207.

    [7] LEDIG C, THEIS L, HUSZAR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017: 105-114.

    [8] 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, 2016, 38(2):295-307.

    [9] LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution[C]//Proceddings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu, HI, USA: IEEE, 2017:136-144.

    [10] LAI W S, HUANG J B, AHUJA N, et al. Deep laplacian pyramid networks for fast and accurate super-resolution[C]//IEEE Conference on Computer Vision&Pattern Recognition. Honolulu, HI, USA: IEEE Computer Society, 2017: 5835-5843.

    [11] WANG H, WU H, HU Q, et al. Underwater image super-resolution using multi-stage information distillation networks[J]. Journal of Visual Communication and Image Representation, 2021, 77(5): 103136.

    [12] CHAN K C K, WANG X T, YU K, et al. BasicVSR: the search for essential components in video super-resolution and beyond[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, USA: IEEE, 2021: 4945-4954.

    [13] WOZNIAK B, DERA J. Light absorption in sea water[M]. New York, USA: Springer, 2007.

    [14] PORTO M T, BRANZAN A A, HOEBERECHTS M A. contrast-guided approach for the enhancement of low-lighting underwater images[J]. Journal of Imaging, 2019, 5(10): 79-103.

    [15] AKKAYNAK D, TREIBITZ T. A revised underwater image formation model[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Salt Lake City, UT, USA: IEEE, 2018: 6723-6732.

    [16] ZHOU J C, WANG Y Y, ZHANG W S, et al. Underwater image restoration via feature priors to estimate background light and optimized transmission map[J]. Optics Express, 2021, 29(18): 28242-28245.

    [17] EDUARDO Q G, DELORY E, CALLICO G M, et al. Underwater video enhancement using multi-camera super-resolution[J]. Optics Communications, 2017, 404: 94-102.

    [18] WEICKERT J. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods[J]. International Journal of Computer Vision, 2005, 61(3): 211-231.

    [19] BURRI M,NIKOLIC J,GOHL P,et al. The euroc micro aerial vehicle datasets[J]. International Journal of Robotics Research,2016,35(10):1157-1163.

    [20] ISLAM M J,LUO P,SATTAR J,et al. Underwater image super-resolutolion using deep residual multipliers[C]//IERE International Conference on Robotics and Automation (ICRA),The Palace of Congress,Paris,France: IEEE, 2020: 900-906.

    [22] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

    ZHAO Yanling, ZHANG Jing, FENG Yingbin. Improved Underwater Video Super-resolution Reconstruction Based on BasicVSR[J]. Electro-Optic Technology Application, 2023, 38(5): 66
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