[1] YANG Z Z, YAO M D, HUANG J et al. SIR-former: stereo image restoration using transformer[C], 6377-6385(2022).

[2] ZHANG X C. Deep learning-based multi-focus image fusion: a survey and a comparative study[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 4819-4838(2021).

[3] XU H, MA J Y, JIANG J J et al. U2Fusion: a unified unsupervised image fusion network[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 502-518(2022).

[4] BANDARA W G C, PATEL V M. HyperTransformer: a textural and spectral feature fusion transformer for Pansharpening[C], 1757-1767(2022).

[5] BHATNAGAR G, WU Q M J, LIU Z. Directive contrast based multimodal medical image fusion in NSCT domain[J]. IEEE Transactions on Multimedia, 15, 1014-1024(2013).

[6] GANASALA P, KUMAR V. Multimodality medical image fusion based on new features in NSST domain[J]. Biomedical Engineering Letters, 4, 414-424(2014).

[7] JAMES A P, DASARATHY B V. Medical image fusion: a survey of the state of the art[J]. Information Fusion, 19, 4-19(2014).

[8] HUANG L, RUAN S, DENŒUX T. Application of belief functions to medical image segmentation: a review[J]. Information Fusion, 91, 737-756(2023).

[9] LI W S, ZHANG Y, WANG G F et al. DFENet: a dual-branch feature enhanced network integrating transformers and convolutional feature learning for multimodal medical image fusion[J]. Biomedical Signal Processing and Control, 80, 104402(2023).

[10] LIANG J Y, CAO J Z, SUN G L et al. SwinIR: image restoration using Swin transformer[C], 1833-1844(2021).

[11] LI J W, CHEN J S, LIU J Y et al. Learning a graph neural network with cross modality interaction for image fusion[C], 4471-4479(2023).

[12] TANG L F, YUAN J T, MA J Y. Image fusion in the loop of high-level vision tasks: a semantic-aware real-time infrared and visible image fusion network[J]. Information Fusion, 82, 28-42(2022).

[13] 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).

[14] HUANG J X, LI X L, TAN T S et al. MMA-UNet: a multi-modal asymmetric UNet architecture for infrared and visible image fusion[J/OL](2024).

[15] VASWANI A, SHAZEER N, PARMAR N et al. Attention is all you need[C], 6000-6010(2017).

[16] DOSOVITSKIY A, BEYER L, KOLESNIKOV A et al. An image is worth 16×16 words: transformers for image recognition at scale[C](2021).

[17] ZAMIR S W, ARORA A, KHAN S et al. Restormer: efficient transformer for high-resolution image restoration[C], 5718-5729(2022).

[18] ZHU H W, KE W J, LI D et al. Dual cross-attention learning for fine-grained visual categorization and object re-identification[C], 4682-4692(2022).

[20] MA J Y, MA Y, LI C. Infrared and visible image fusion methods and applications: a survey[J]. Information Fusion, 45, 153-178(2019).

[21] ZHAO Z X, BAI H W, ZHANG J S et al. CDDFuse: correlation-driven dual-branch feature decomposition for multi-modality image fusion[C], 5906-5916(2023).

[22] ZHAO Z X, BAI H W, ZHANG J S et al. Equivariant multi-modality image fusion[C], 25912-25921(2024).

[23] 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).

[24] LI H, WU X J, KITTLER J. RFN-Nest: an end-to-end residual fusion network for infrared and visible images[J]. Information Fusion, 73, 72-86(2021).

[25] LIU S H, LAN X X, CHEN W Y et al. AGMFusion: a real-time end-to-end infrared and visible image fusion network based on adaptive guidance module[J]. IEEE Sensors Journal, 24, 28338-28350(2024).

[26] 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).