[1] Zhibing GONG. A new type of gyroscope for the 21st century-fiber optic gyroscope. Journal of Applied Optics, 18, 37-41(1997).

[2] Hong TAO, Jian LIU, Jinwen TIAN. A super-resolution image processing algorithm based on wavelet transform and interpolation. Journal of Wuhan University of Technology, 63-66(2002).

[3] J C YANG, J WRIGHT, T S HUANG. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 19, 2861-2873(2010).

[4] [4] HUANG J B, SINGH A, AHUJA N. Single image superresolution from transfmed selfexemplars [C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, Boston, MA, USA. June 712, 2015, New Yk: IEEE, 2015: 51975206.

[5] YANG?Shuguang. A perceptually enhanced super-resolution reconstruction model based on depth back projection. Journal of Applied Optics, 42, 691-697(2021).

[6] C DONG, C C LOY, K HE, et al. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

[7] [7] KIM J, LEE J K, LEE K M. Accurate image superresolution using very deep convolutional wks [C] Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, , Las Vegas, NV, USA, June 2730, 2016. New Yk: IEEE, 2016: 16461654.

[8] [8] LIM B, SON S, KIM H. Enhanced deep residual wks f single image superresolution [C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, Honolulu, HI, USA, July 2126, 2017. New Yk: IEEE, 2017: 11321140.

[9] Qingqing GAO, Jianwei ZHAO, Zhenghua ZHOU. Image super-resolution reconstruction based on recurrent multiscale convolutional networks. Pattern Recognition and Artificial Intelligence, 33, 972-980(2020).

[10] [10] ZHENG Jingyang. Research application of image superresolution algithms based on dense residual wks [D]. Chongqing: Chongqing Nmal University, 2020.

[11] Lin GUO, Chen LI, Chen CHEN, . Image super-resolution reconstruction based on channel attention recurrent residual network. Computer Science, 48, 139-144(2021).

[12] [12] TAI Y S, YANG J X, LIU X. Image superresolution via deep recursive residual wk[C] Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, Honolulu, HI, USA, July 2126, 2017. New Yk: IEEE, 2017: 31473155.

[13] [13] HOWARD A, ZHU M, CHEN B, et al. Mobiles: efficient convolutional neural wks f mobile vision applications. (20170417)[20220118]. https:arxiv.gabs1704.04861

[14] [14] ZHANG X, ZHOU X, LIN M. Shuffle: an extremely efficient convolutional neural wk f mobile devices[C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, Salt Lake City, UT, USA , June 1822, 2018. New Yk: IEEE, 2018: 68486856.

[15] W YANG, X ZHANG, Y TIAN, et al. Lcscnet: linear compressing-based skip-connecting network for image super-resolution. IEEE Transactions on Image Processing, 29, 1450-1464(2020).

[16] Xiaoqiang ZHAO, Zhaoyang SONG. Adam-optimized CNN super-resolution reconstruction. Journal of Frontiers of Computer Science and Technology, 13, 858-865(2019).

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

[18] [18] LAI W S, HUANG J B, AHUJA N, et al. Deep laplacian pyra wks f fast accurate superresolution[C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, Honolulu, HI, USA, July 2126, 2017. New Yk: IEEE, 2017: 624632.

[19] [19] ZHANG K. ZUO W M, ZHANG L. Deep plugplay superresolution f arbitrary blur kernels [C]Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, Long Beach, CA, USA, June1620, 2019. New Yk: IEEE, 2019: 16711681.

[20] H R SHEIKH, A C BOVIK, G VECIANA. An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Transactions on Image Processing, 14, 2117-2128(2005).