[1] Tang L F, Zhang H, Xu H et al. Deep learning-based image fusion: a survey[J]. Journal of Image and Graphics, 28, 3-36(2023).

[2] Li Y Y, Sun Y J, Huang X H et al. An image fusion method based on sparse representation and sum modified-Laplacian in NSCT domain[J]. Entropy, 20, 522(2018).

[3] Xu L N, Xie G Q, Zhou S T. Panchromatic and multispectral image fusion combining GIHS, NSST, and PCA[J]. Applied Sciences, 13, 1412(2023).

[4] Li J J, Wang Q. Intra-group and inter-group electrocardiograph coding image fusion and classification based on multi-scale group convolution feature fusion network[J]. Biomedical Signal Processing and Control, 72, 103374(2022).

[5] Stone J V[M]. Independent component analysis: a tutorial introduction(2004).

[6] Wang Z B, Cui Z J, Zhu Y. Multi-modal medical image fusion by Laplacian pyramid and adaptive sparse representation[J]. Computers in Biology and Medicine, 123, 103823(2020).

[7] Chao Z, Duan X G, Jia S F et al. Medical image fusion via discrete stationary wavelet transform and an enhanced radial basis function neural network[J]. Applied Soft Computing, 118, 108542(2022).

[8] Bhatnagar G, Wu Q M, Liu Z. Directive contrast based multimodal medical image fusion in NSCT domain[J]. IEEE Transactions on Multimedia, 15, 1014-1024(2013).

[9] Liu Y, Chen X, Cheng J et al. A medical image fusion method based on convolutional neural networks[C], 10-13(2017).

[10] Zhang Y, Liu Y, Sun P et al. IFCNN: a general image fusion framework based on convolutional neural network[J]. Information Fusion, 54, 99-118(2020).

[11] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[12] Tang W, Liu Y, Cheng J et al. Green fluorescent protein and phase contrast image fusion via detail preserving cross network[J]. IEEE Transactions on Computational Imaging, 7, 584-597(2021).

[13] Wang H, Luo X Q, Zhang Z C. Infrared and visible image fusion based on separate expression of mutual information features[J]. Laser & Optoelectronics Progress, 60, 2410002(2023).

[14] Ma J Y, Tang L F, Fan F et al. SwinFusion: cross-domain long-range learning for general image fusion via Swin Transformer[J]. IEEE/CAA Journal of Automatica Sinica, 9, 1200-1217(2022).

[15] Liu Z, Lin Y T, Cao Y et al. Swin Transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2021).

[16] Qu L H, Liu S L, Wang M N et al. Trans2Fuse: empowering image fusion through self-supervised learning and multi-modal transformations via Transformer networks[J]. Expert Systems with Applications, 236, 121363(2024).

[17] Hao W Y, Cai H Y, Zuo T T et al. Intravascular ultrasound image segmentation fusing Transformer branch and topology enforcement[J]. Laser & Optoelectronics Progress, 61, 1237008(2024).

[18] Wang C W, Wu J S, Fang A Q et al. An efficient frequency domain fusion network of infrared and visible images[J]. Engineering Applications of Artificial Intelligence, 133, 108013(2024).

[19] Zheng N S, Zhou M, Huang J et al. Frequency integration and spatial compensation network for infrared and visible image fusion[J]. Information Fusion, 109, 102359(2024).

[20] Kuppuraj T, Saini R, Jayadurga R et al. A comparative analysis of wavelet and Fourier transform for image fusion[C], 24-28(2024).

[21] Yu H, Zheng N S, Zhou M et al. Frequency and spatial dual guidance for image dehazing[M]. Computer vision‒ECCV 2022, 13679, 181-198(2022).

[22] Liang Y J, Cao Z H, Deng L J et al. Fourier-enhanced implicit neural fusion network for multispectral and hyperspectral image fusion[EB/OL]. https:∥arxiv.org/abs/2404.15174v1

[23] Jung H, Kim Y, Jang H et al. Unsupervised deep image fusion with structure tensor representations[J]. IEEE Transactions on Image Processing, 29, 3845-3858(2020).

[24] Jiang L M, Dai B, Wu W et al. Focal frequency loss for image reconstruction and synthesis[C], 13899-13909(2021).

[25] Liu H, Zhu J C, Wang X Y et al. Multi-scale feature frequency domain decomposition filtering for medical image fusion[J]. Journal of Software, 35, 5687-5709(2024).

[26] Zhao W D, Wang D, Lu H C. Multi-focus image fusion with a natural enhancement via a joint multi-level deeply supervised convolutional neural network[J]. IEEE Transactions on Circuits and Systems for Video Technology, 29, 1102-1115(2019).

[27] Li S T, Yang B. Multifocus image fusion using region segmentation and spatial frequency[J]. Image and Vision Computing, 26, 971-979(2008).

[28] Shapley R, Lennie P. Spatial frequency analysis in the visual system[J]. Annual Review of Neuroscience, 8, 547-583(1985).

[29] Piella G, Heijmans H. A new quality metric for image fusion[C], 14-17(2003).

[30] Manjusha D, Udhav B. Image fusion and image quality assessment of fused images[J]. International Journal of Image Processing, 4, 484(2010).

[31] Sheikh H R, Bovik A C. A visual information fidelity approach to video quality assessment[C], 7, 2117-2128(2005).

[32] Tan W, Tiwari P, Pandey H M et al. Multimodal medical image fusion algorithm in the era of big data[J/OL]. Neural Computing and Applications(2020). https:∥link.springer.com/article/10.1007/s00521-020-05173-2

[33] Li B, Peng H, Luo X H et al. Medical image fusion method based on coupled neural P systems in nonsubsampled shearlet transform domain[J]. International Journal of Neural Systems, 31, 2050050(2021).

[34] 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).

[35] Ma J Y, Xu H, Jiang J J et al. DDcGAN: a dual-discriminator conditional generative adversarial network for multi-resolution image fusion[J]. IEEE Transactions on Image Processing, 29, 4980-4995(2020).

[36] Xu H, Ma J Y. EMFusion: an unsupervised enhanced medical image fusion network[J]. Information Fusion, 76, 177-186(2021).

[37] Tang W, He F Z, Liu Y et al. MATR: multimodal medical image fusion via multiscale adaptive transformer[J]. IEEE Transactions on Image Processing, 31, 5134-5149(2022).

[38] Tang L F, Yuan J T, Zhang H et al. PIAFusion: a progressive infrared and visible image fusion network based on illumination aware[J]. Information Fusion, 83, 79-92(2022).

[39] Li J W, Liu J Y, Zhou S H et al. GeSeNet: a general semantic-guided network with couple mask ensemble for medical image fusion[J]. IEEE Transactions on Neural Networks and Learning Systems, 35, 16248-16261(2024).

[40] Qu L H, Liu S L, Wang M N et al. TransMEF: a Transformer-based multi-exposure image fusion framework using self-supervised multi-task learning[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2126-2134(2022).

[41] Tang W, He F Z. FATFusion: a functional–anatomical Transformer for medical image fusion[J]. Information Processing & Management, 61, 103687(2024).