[1] Zhang H R, Li Y B, Xing R K et al. Evaluation of air defense missile infrared camouflage capability based on set pair analysis[J]. Laser & Optoelectronics Progress, 55, 070402(2018).
[2] Guo T, Hua W S, Liu X et al. Comprehensive evaluation of optical camouflage effect based on hyperspectra[J]. Laser & Optoelectronics Progress, 53, 101002(2016).
[3] Bai X Q, Liao N F, Huang H et al. Evaluation of ship camouflage effect on sea based on color difference and spectral characteristics[J]. Laser & Optoelectronics Progress, 55, 093301(2018).
[4] Cai W, Wu F C, Yang Z Y et al. Research on magneto-optic modulation technology and application[J]. Laser & Optoelectronics Progress, 52, 060003(2015).
[5] Wang T, Niu M S, Bu M M et al. Polarization-difference imaging system with adjustable optical path and its characteristics[J]. Acta Optica Sinica, 37, 0711001(2017).
[6] Tao F, Song M X, Hong J et al. Polarization calibration method for simultaneous imaging polarimeter based on off-axis three-mirror[J]. Acta Optica Sinica, 38, 0912005(2018).
[7] Wang X L, Wang F, Liu X et al. Hyperspectral polarization characteristics of typical camouflage target under desert background[J]. Laser & Optoelectronics Progress, 55, 051101(2018).
[8] Cui B S, Xue S Q, Ji Y J et al. Camouflage effectiveness evaluation based on image feature[J]. Infrared and Laser Engineering, 39, 1178-1183(2010).
[9] Xu W D, Lü X L, Chen B et al. A model based on texture analysis for the performance evaluation of camouflage screen equipment[J]. Acta Armamentarii, 23, 329-331(2002).
[10] Lin W, Chen Y H, Wang J Y et al. Camouflage assessment method based on image features and psychological perception quantity[J]. Acta Armamentarii, 34, 412-417(2013).
[11] Wang P Y, Zhao D H, Li M F. Optical camouflage effect assessment based on digital image inpainting technology[J]. Laser & Optoelectronics Progress, 55, 031011(2018).
[12] Zhang R, Li W P, Mo T. Review of deep learning[J]. Information and Control, 47, 385-397, 410(2018).
[13] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017). http://dl.acm.org/citation.cfm?id=2999257
[14] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 770-778(2016).
[15] Huang G, Liu Z. Maaten L V D, et al. Densely connected convolutional networks. [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2261-2269(2017).
[16] Xin Y, Yang J, Xie Z Q. A semantic overlapping community detecting algorithm in social networks based on random walk[J]. Journal of Computer Research and Development, 52, 499-511(2015).
[17] Wang Y, Tang J, Rao Q F et al. High efficient K-means algorithm for determining optimal number of clusters[J]. Journal of Computer Applications, 34, 1331-1335(2014).
[18] An Z, Xu X P, Yang J H et al. Design of augmented reality head-up display system based on image semantic segmentation[J]. Acta Optica Sinica, 38, 0710004(2018).
[19] Li S M, Lei G Q, Fan R. Depth map super-resolution based on two-channel convolutional neural network[J]. Acta Optica Sinica, 38, 1010002(2018).
[20] Sun H Q, Pang Y W. An neural network framework of self-learning uncertainty[J]. Acta Optica Sinica, 38, 0620002(2018).
[21] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 3431-3440(2015).
[22] Isola P, Zhu J Y, Zhou T H et al. Image-to-image translation with conditional adversarial networks. [C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 5967-5976(2017).
[23] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative adversarial nets. [C]∥Advances in Neural Information Processing Systems, 2672-2680(2014).