Research on Multi-Frame Sea Surface Image Super Resolution Algorithm Based on Deep Learning

Yushuang Zhang; Wenbo Han; Danfei Huang; Liying Zhao; Aiqi Zhong

doi:10.3788/LOP202158.1610005

[1] Zhou J H, Zhou C, Zhu J J et al. A method of super-resolution reconstruction for remote sensing image based on non-subsampled contourlet transform[J]. Acta Optica Sinica, 35, 0110001(2015).

[2] Liu K W, Ma Y, Xiong H X et al. Medical-image super-resolution reconstruction method based on residual channel attention network[J]. Laser & Optoelectronics Progress, 57, 021014(2020).

[3] Ur H, Gross D. Improved resolution from subpixel shifted pictures[J]. CVGIP: Graphical Models and Image Processing, 54, 181-186(1992).

[4] Dong C, Loy C C, He K M et al. Learning a deep convolutional network for image super-resolution[M]. //Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science, 8692, 184-199(2014).

[5] Ledig C, Theis L, Huszár 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), July 21-26, 2017, Honolulu, HI, USA., 105-114(2017).

[6] Yuan P Y, Zhang Y P. Image super-resolution reconstruction method using dual discriminator based on generative adversarial networks[J]. Laser & Optoelectronics Progress, 56, 231010(2019).

[7] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 1637-1645(2016).

[8] Shi Z T, Wang Z R, Wang R et al. Single image super-resolution based on convolutional neural network[J]. Laser & Optoelectronics Progress, 55, 121001(2018).

[9] Li S M, Lei G Q, Fan R. Depth map super-resolution reconstruction based on convolutional neural networks[J]. Acta Optica Sinica, 37, 1210002(2017).

[10] Sun C, Lü J W, Li J W et al. Method of rapid image super-resolution based on deconvolution[J]. Acta Optica Sinica, 37, 1210004(2017).

[11] Jo Y, Oh S W, Kang J et al. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 3224-3232(2018).

[12] Tao X, Gao H Y, Liao R J et al. Detail-revealing deep video super-resolution[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 4482-4490(2017).

[13] Caballero J, Ledig C, Aitken A et al. Real-time video super-resolution with spatio-temporal networks and motion compensation[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2848-2857(2017).

[14] Xue T F, Chen B A, Wu J J et al. Video enhancement with task-oriented flow[J]. International Journal of Computer Vision, 127, 1106-1125(2019).

[15] Ranjan A, Black M J. Optical flow estimation using a spatial pyramid network[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2720-2729(2017).

[16] Jaderberg M, Simonyan K, Zisserman A. Spatial transformer networks[C]. //Advances in Neural Information Processing Systems(NIPS), December 11-12, 2015, Canada, Montreal, 2017-2025(2015).

[17] He K M, Zhang X Y, Ren S Q et al. Identity mappings in deep residual networks[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9908, 630-645(2016).