[1] Zhou X L, Jiang Z T. Infrared and visible image fusion combining pulse-coupled neural network and guided filtering[J]. Acta Optica Sinica, 39, 1110003(2019).
[2] Liang J M, Yang S, Tian L F. Infrared and visible image fusion based on image enhancement and rolling guidance filtering[J]. Laser & Optoelectronics Progress, 60, 0210006(2023).
[3] Lu X, Yang L, Li M et al. Infrared and visible image fusion method based on Tikhonov regularization and detail reconstruction[J]. Acta Optica Sinica, 40, 0210001(2020).
[4] Jin H Y, Wang Y Y. A fusion method for visible and infrared images based on contrast pyramid with teaching learning based optimization[J]. Infrared Physics & Technology, 64, 134-142(2014).
[5] Li H, Wu X J, Kittler J. MDLatLRR: a novel decomposition method for infrared and visible image fusion[J]. IEEE Transactions on Image Processing, 4733-4746(2020).
[6] Yang F Y, Wang M. Infrared and visible image fusion based on structure-texture decomposition and VGG deep networks[J]. Laser & Optoelectronics Progress, 60, 0210008(2023).
[7] Miao Z, Zhang Y, Li W H. Modeling method of infrared target based on double countermeasure self-coding network[J]. Acta Optica Sinica, 40, 1111002(2020).
[8] Li H, Wu X J. DenseFuse: a fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 28, 2614-2623(2018).
[9] Li H, Wu X J, Durrani T. NestFuse: an infrared and visible image fusion architecture based on nest connection and spatial/channel attention models[J]. IEEE Transactions on Instrumentation and Measurement, 69, 9645-9656(2020).
[10] Jian L H, Yang X M, Liu Z et al. SEDRFuse: a symmetric encoder-decoder with residual block network for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 70, 1-15(2021).
[11] Luo X Q, Gao Y H, Wang A Q et al. IFSepR: a general framework for image fusion based on separate representation learning[J]. IEEE Transactions on Multimedia, 25, 608-623(2023).
[12] Xu H, Wang X Y, Ma J Y. DRF: disentangled representation for visible and infrared image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 70, 5006713(2021).
[13] Sanchez E H, Serrurier M, Ortner M. Learning disentangled representations via mutual information estimation[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science, 12367, 205-221(2020).
[15] Ma J Y, Chen C, Li C et al. Infrared and visible image fusion via gradient transfer and total variation minimization[J]. Information Fusion, 31, 100-109(2016).
[16] Ma J Y, Zhang H, Shao Z F et al. GANMcC: a generative adversarial network with multiclassification constraints for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 70, 5005014(2021).
[17] Zhang H, Yuan J T, Tian X et al. GAN-FM: infrared and visible image fusion using GAN with full-scale skip connection and dual Markovian discriminators[J]. IEEE Transactions on Computational Imaging, 7, 1134-1147(2021).
[18] Zhang H, Ma J Y. SDNet: a versatile squeeze-and-decomposition network for real-time image fusion[J]. International Journal of Computer Vision, 129, 2761-2785(2021).