[1] 1刘先红， 陈志斌， 秦梦泽. 结合引导滤波和卷积稀疏表示的红外与可见光图像融合［J］. 光学 精密工程， 2018， 26（5）： 1242-1253. doi: 10.3788/OPE.20182605.1242LIUX H， CHENZ B， QINM Z. Infrared and visible image fusion using guided filter and convolutional sparse representation［J］. Optics and Precision Engineering， 2018， 26（5）： 1242-1253.（in Chinese）. doi: 10.3788/OPE.20182605.1242
[2] H LI, Q WANG, H WANG et al. Infrared small target detection using tensor based least mean square. Computers & Electrical Engineering, 91, 106994(2021).
[3] J Y MA, Y MA, C LI. Infrared and visible image fusion methods and applications： a survey. Information Fusion, 45, 153-178(2019).
[4] P H DINH. Combining Gabor energy with equilibrium optimizer algorithm for multi-modality medical image fusion. Biomedical Signal Processing and Control, 68, 102696(2021).
[5] S LIU, H ZHAO, Q DU et al. Novel cross-resolution feature-level fusion for joint classification of multispectral and panchromatic remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 1-13(2021).
[6] W LIU, Z F WANG. A novel multi-focus image fusion method using multiscale shearing non-local guided averaging filter. Signal Processing, 166, 107252(2020).
[7] Q ZHANG, Y LIU, R S BLUM et al. Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images： a review. Information Fusion, 40, 57-75(2018).
[8] 8于国栋， 王春阳， 兰孝野， 等. 基于离线标定的快速图像拼接算法［J］. 液晶与显示， 2021， 36（10）： 1430-1436. doi: 10.37188/CJLCD.2020-0330YUG D， WANGC Y， LANX Y， et al. Fast image mosaic algorithm based on offline calibration［J］. Chinese Journal of Liquid Crystals and Displays， 2021， 36（10）： 1430-1436.（in Chinese）. doi: 10.37188/CJLCD.2020-0330
[9] S H LIU, B TREMBLAIS, P CARRE et al. Image reconstruction from multiscale singular points based on the dual-tree complex wavelet transform. Security and Communication Networks, 2021, 6752486(2021).
[10] X S LI, F Q ZHOU, H S TAN et al. Multi-focus image fusion based on nonsubsampled contourlet transform and residual removal. Signal Processing, 184, 108062(2021).
[11] 11刘博， 韩广良， 罗惠元. 基于多尺度细节的孪生卷积神经网络图像融合算法［J］. 液晶与显示， 2021， 36（9）： 1283-1293. doi: 10.37188/CJLCD.2020-0339LIUB， HANG L， LUOH Y. Image fusion algorithm based on multi-scale detail Siamese convolutional neural network［J］. Chinese Journal of Liquid Crystals and Displays， 2021， 36（9）： 1283-1293.（in Chinese）. doi: 10.37188/CJLCD.2020-0339
[12] Y J ZHENG, S C LIU, Q DU et al. A novel multitemporal deep fusion network （MDFN） for short-term multitemporal HR images classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10691-10704(2021).
[13] 13杨艳春， 高晓宇， 党建武， 等. 基于WEMD和生成对抗网络重建的红外与可见光图像融合［J］. 光学 精密工程， 2022， 30（3）： 320-330. doi: 10.37188/OPE.20223003.0320YANGY C， GAOX Y， DANGJ W， et al. Infrared and visible image fusion based on WEMD and generative adversarial network reconstruction［J］. Optics and Precision Engineering， 2022， 30（3）： 320-330.（in Chinese）. doi: 10.37188/OPE.20223003.0320
[14] T T YAO, J C HU, B ZHANG et al. Scale and appearance variation enhanced Siamese network for thermal infrared target tracking. Infrared Physics & Technology, 117, 103825(2021).
[15] Y LIU, X CHEN, H PENG et al. Multi-focus image fusion with a deep convolutional neural network. Information Fusion, 36, 191-207(2017).
[16] J Y MA, W YU, P W LIANG et al. FusionGAN： a generative adversarial network for infrared and visible image fusion. Information Fusion, 48, 11-26(2019).
[17] K R PRABHAKAR, V S SRIKAR, R V BABU. Deepfuse： A deep unsupervised approach for exposure fusion with extreme exposure image pairs, 4724-4732(2017).
[18] H LI, X J WU, T S DURRANI. Infrared and visible image fusion with ResNet and zero-phase component analysis. Infrared Physics & Technology, 102, 103039(2019).
[19] W B AN, H M WANG. Infrared and visible image fusion with supervised convolutional neural network. Optik, 219, 165120(2020).
[20] W Q ZHU, X Y TANG, R ZHANG et al. Infrared and visible image fusion based on edge-preserving and attention generative adversarial network. Journal of Infrared and Millimeter Waves, 40, 696-708(2021).
[21] X GUO, L Y MENG, L MEI et al. Multi-focus image fusion with Siamese self-attention network. IET Image Processing, 14, 1339-1346(2020).
[22] 22张红颖， 安征. 基于改进双流时空网络的人体行为识别［J］. 光学 精密工程， 2021， 29（2）： 420-429. doi: 10.37188/OPE.20212902.0420ZHANGH Y， ANZ. Human action recognition based on improved two-stream spatiotemporal network［J］. Optics and Precision Engineering， 2021， 29（2）： 420-429.（in Chinese）. doi: 10.37188/OPE.20212902.0420
[23] B Z WEI, X C FENG, K WANG et al. The multi-focus-image-fusion method based on convolutional neural network and sparse representation. Entropy （Basel， Switzerland）, 23, 827(2021).
[24] H LI, X J WU, J KITTLER. MDLatLRR： a novel decomposition method for infrared and visible image fusion. IEEE Transactions on Image Processing, 29, 4733-4746(2020).
[25] C GAO, C C SONG, Y C ZHANG et al. Improving the performance of infrared and visible image fusion based on latent low-rank representation nested with rolling guided image filtering. IEEE Access, 9, 91462-91475(2021).
