| ［1］ |
齐彬,刘方. 人造目标SAR图像实时仿真［J］. 系统仿真学报， 2008,20(1):186-190.
|
| ［2］ |
任苗苗,潘卓,徐向辉,等. 建筑物的高分辨率SAR图像仿真方法［J］. 中国科学院大学学报, 2018,35(6):788-794.
|
| ［3］ |
LIU W L, ZHAO Y J, LIU M, et al. Generating simulated SAR images using Generative Adversarial Network［C］// Applications of Digital Image Processing XLI. 2018.
|
| ［4］ |
CHA M, MAJUMDAR A, KUNG H T, et al. Improving SAR automatic target recognition using simulated images under deep residual refinements［C］// 2018 IEEE International Conference on Acoustics, Speech and Signal Processing. 2018:2606-2610.
|
| ［5］ |
LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015:3431-3440.
|
| ［6］ |
GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets［C］// Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014:2672-2680.
|
| ［7］ |
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.
|
| ［8］ |
KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:1646-1654.
|
| ［9］ |
TAIGMAN Y, POLYAK A, WOLF L. Unsupervised Cross-Domain Image Generation［DB/OL］. (2016-11-07)［2020-02-17］. https://arxiv.org/abs/1611.02200.
|
| ［10］ |
LUC P, COUPRIE C, CHINTALA S, et al. Semantic Segmentation Using Adversarial Networks［DB/OL］. (2016-11-25)［2020-02-17］. https://arxiv.org/abs/1611.08408.
|
| ［11］ |
SOULY N, SPAMPINATO C, SHAH M. Semi supervised semantic segmentation using generative adversarial network［C］// Proceedings of the IEEE International Conference on Computer Vision. 2017:5688-5696.
|
| ［12］ |
YI Z L, ZHANG H, TAN P, et al. Dualgan: Unsupervised dual learning for image-to-image translation［C］// Proceedings of the IEEE International Conference on Computer Vision. 2017:2849-2857.
|
| ［13］ |
RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segmentation［C］// International Conference on Medical Image Computing & Computer-assisted Intervention. 2015:234-241.
|
| ［14］ |
ISOLA P, ZHU J Y, ZHOU T H, et al. Image-to-image translation with conditional adversarial networks［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:5967-5976.
|
| ［15］ |
MIYATO T, KATAOKA T, KOYAMA M, et al. Spectral normalization for generative adversarial networks［C］// International Conference on Learning Representations. 2018.
|
| ［16］ |
SCAMAN K, VIRMAUX A. Lipschitz regularity of deep neural networks: Analysis and efficient estimation［C］// Proceedings of the 32nd Conference on Neural Information Processing Systems. 2018:3835-3844.
|
| ［17］ |
ZHANG L, ZHANG L, MOU X Q, et al. FSIM: A feature similarity index for image quality assessment［J］. IEEE Transactions on Image Processing, 2011,20(8):2378-2386.
|
| ［18］ |
WANG Z, LI Q. Information content weighting for perceptual image quality assessment［J］. IEEE Transactions on Image Processing, 2011,20(5):1185-1198.
|
| ［19］ |
XUE W F, ZHANG L, MOU X Q, et al. Gradient magnitude similarity deviation: A highly efficient perceptual image quality index［J］. IEEE Transactions on Image Processing, 2014,23(2):684-695.
|
| ［20］ |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks［C］// Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012:1097-1105.
|
| ［21］ |
SIMONYAN K, ZISSERMAN A. Very Deep Convolutional Networks for Large-scale Image Recognition［DB/OL］. (2014-09-04)［2020-02-17］. https://arxiv.org/abs/1409.1556.
|
| ［22］ |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-778.
|
