[1] Ma J Y, Ma Y, Li C. Infrared and visible image fusion methods and applications:a survey[J]. Information Fusion, 2019, 45: 153-178.

[2] Ranchin T, Wald L. The wavelet transform for the analysis of remotely sensed images[J]. International Journal of Remote Sensing, 1993, 14(3): 615-619.

[3] KingsburyN. A dual-tree complex wavelet transform with improved orthogonality and symmetry properties[C]∥Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), September 10-13, 2000, Vancouver, BC, Canada. New York: IEEE, 2000: 375- 378.

[4] Liu Y, Liu S P, Wang Z F. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015, 24: 147-164.

[5] 易维, 曾湧, 原征. 基于NSCT变换的高分三号SAR与光学图像融合[J]. 光学学报, 2018, 38(11): 1110002.

    Yi W, Zeng Y, Yuan Z. Fusion of GF-3 SAR and optical images based on the nonsubsampled contourlet transform[J]. Acta Optica Sinica, 2018, 38(11): 1110002.

[6] Hu J W, Li S T. The multiscale directional bilateral filter and its application to multisensor image fusion[J]. Information Fusion, 2012, 13(3): 196-206.

[7] 丁文杉, 毕笃彦, 何林远, 等. 基于剪切波变换和邻域结构特征的红外与可见光图像融合[J]. 光学学报, 2017, 37(10): 1010002.

    Ding W S, Bi D Y, He L Y, et al. Fusion of infrared and visible images based on shearlet transform and neighborhood structure features[J]. Acta Optica Sinica, 2017, 37(10): 1010002.

[8] Zhang Q, Liu Y, Blum R S, et al. Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: a review[J]. Information Fusion, 2018, 40: 57-75.

[9] 朱达荣, 许露, 汪方斌, 等. 基于快速有限剪切波变换与引导滤波的多聚焦图像融合算法[J]. 激光与光电子学进展, 2018, 55(1): 011001.

    Zhu D R, Xu L, Wang F B, et al. Multi-focus image fusion algorithm based on fast finite shearlet transform and guided filter[J]. Laser & Optoelectronics Progress, 2018, 55(1): 011001.

[10] Liu Y, Chen X, Wang Z F, et al. Deep learning for pixel-level image fusion: recent advances and future prospects[J]. Information Fusion, 2018, 42: 158-173.

[11] Liu Y, Chen X, Peng H, et al. Multi-focus image fusion with a deep convolutional neural network[J]. Information Fusion, 2017, 36: 191-207.

[12] 蔺素珍, 韩泽. 基于深度堆叠卷积神经网络的图像融合[J]. 计算机学报, 2017, 40(11): 2506-2518.

    Lin S Z, Han Z. Images fusion based on deep stack convolutional neural network[J]. Chinese Journal of Computers, 2017, 40(11): 2506-2518.

[13] LiH, Wu XJ, KittlerJ. Infrared and visible image fusion using a deep learning framework[C]∥2018 24th International Conference on Pattern Recognition (ICPR), August 20-24, 2018, Beijing, China. New York: IEEE, 2018: 2705- 2710.

[14] Liu Y, Chen X, Ward R K, et al. Image fusion with convolutional sparse representation[J]. IEEE Signal Processing Letters, 2016, 23(12): 1882-1886.

[15] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 770- 778.

[16] Jiang X H, Pang Y W, Li X L, et al. Deep neural networks with elastic rectified linear units for object recognition[J]. Neurocomputing, 2018, 275: 1132-1139.

[17] Cai J R, Gu S H, Zhang L. Learning a deep single image contrast enhancer from multi-exposure images[J]. IEEE Transactions on Image Processing, 2018, 27(4): 2049-2062.

[18] ZhangH, DanaK. Multi-style generative network for real-time transfer[M] ∥Leal-Taixé L, Roth S.Computer Vision-ECCV 2018 Workshops. Lecture notes in computer science. Cham: Springer, 2019, 11132: 349- 365.

[19] Goodfellow IJ, Pouget-AbadieJ, MirzaM, et al.Generative adversarial nets[C]∥Proceeding NIPS'14 Proceedings of the 27th International Conference on Neural Information Processing Systems, December 08-13, 2014, Montreal, Canada.Cambridge: MIT Press, 2014, 2: 2672- 2680.

[20] RadfordA, MetzL, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networls[J/OL]. ( 2016-01-07)[2018-12-25]. https:∥arxiv.org/abs/1511. 06434.

[21] Mao XD, LiQ, Xie HR, et al. Least squares generative adversarial networks[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 2813- 2821.

[22] LedigC, TheisL, HuszárF, 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. New York: IEEE, 2017: 105- 114.

[23] Li Y, Wang N, Shi J, et al. Adaptive Batch Normalization for practical domain adaptation[J]. Pattern Recognition, 2016, 80: 109-117.

[24] Wang S H, Phillips P, Sui Y X, et al. Classification of alzheimer's disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling[J]. Journal of Medical Systems, 2018, 42(5): 85.

[25] HuangR, ZhangS, Li TY, et al. Beyond face rotation: global and local perception GAN for photorealistic and identity preserving frontal view synthesis[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 2458- 2467.

[26] Shi J G, Liu X, Zong Y, et al. Hallucinating face image by regularization models in high-resolution feature space[J]. IEEE Transactions on Image Processing, 2018, 27(6): 2980-2995.

[27] Toet A. The TNO multiband image data collection[J]. Data in Brief, 2017, 15: 249-251.

[28] 黄福升. 多波段图像多尺度变换融合方法比较[D]. 太原: 中北大学, 2018.

    Huang FS. Comparision of multiscale transform fusion methods for multiband image[D]. Taiyuan: The North University of China, 2018.

[29] Jagalingam P, Hegde A V. A review of quality metrics for fused image[J]. Aquatic Procedia, 2015, 4: 133-142.