• Infrared and Laser Engineering
  • Vol. 53, Issue 11, 20240305 (2024)
Kunyu SI and Chunhui NIU
Author Affiliations
  • School of Instrument Science and Photoelectric Engineering, Beijing Information Science & Technology University, Beijing 100192, China
  • show less
    DOI: 10.3788/IRLA20240305 Cite this Article
    Kunyu SI, Chunhui NIU. Triple multi-modal image fusion algorithm based on mixed difference convolution and efficient vision Transformer network[J]. Infrared and Laser Engineering, 2024, 53(11): 20240305 Copy Citation Text show less
    References

    [1] F ZHANG, H PENG, L YU et al. Dual-modality space-time memory network for RGBT tracking. IEEE Transactions on instrumentation and Measurement, 72, 1-11(2023).

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

    [3] J CHEN, X LI, L LUO et al. Infrared and visible image fusion based on target-enhanced multiscale transform decomposition. Information Sciences, 508, 64-78(2020).

    [4] Q ZHANG, Y LIU, R 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).

    [5] H LI, X J WU. DenseFuse: a fusion approach to infrared and visible images. IEEE Transactions on Image Processing, 28, 2614-2623(2018).

    [6] H LI, X J WU, T DURRANI. NestFuse: An infrared and visible image fusion architecture based on nest connection and spatial/channel attention models. IEEE Transactions on Instrumentation and Measurement, 69, 9645-9656(2020).

    [7] H XU, J MA, J JIANG et al. U2Fusion: A unified unsupervised image fusion network. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 502-518(2020).

    [8] J MA, W YU, P LIANG et al. FusionGAN: A generative adversarial network for infrared and visible image fusion. Information Fusion, 48, 11-26(2019).

    [9] J MA, H XU, J JIANG et al. DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Transactions on Image Processing, 29, 4980-4995(2020).

    [10] SU Z, LIU W, YU Z, et al. Pixel difference wks f efficient edge detection[C]Proceedings of the IEEECVF International Conference on Computer Vision, 2021: 51175127.

    [11] A VASWANI, N SHAZEER, N PARMAR et al. Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008(2017).

    [12] Z WANG, Y CHEN, W SHAO et al. SwinFuse: A residual swin transformer fusion network for infrared and visible images. IEEE Transactions on Instrumentation and Measurement, 71, 1-12(2022).

    [13] ZHAO Z, Bai H, Zhang J, et al. Cddfuse: Crelationdriven dualbranch feature decomposition f multimodality image fusion[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition, 2023: 59065916.

    [14] LIU X, PENG H, ZHENG N, et al. EfficientViT: Memy efficient vision transfmer with caded group attention[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition, 2023: 1442014430.

    [15] HOU Q, ZHOU D, Feng J. Codinate attention f efficient mobile wk design[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition, 2021: 1371313722.

    [16] Y YU, Y ZHANG, Z CHENG et al. MCA: Multidimensional collaborative attention in deep convolutional neural networks for image recognition. Engineering Applications of Artificial Intelligence, 126, 107079(2023).

    [17] Z XIANG, J HAO, G QI et al. MFST: Multi-modal feature self-adaptive transformer for infrared and visible image fusion. Remote Sensing, 14, 3233-3233(2022).

    [18] S KARIM, G TONG, J Li et al. MTDFusion: A multilayer triple dense network for infrared and visible image fusion. IEEE Transactions on Instrumentation and Measurement, 73, 1-17(2023).

    [19] J MA, L TANG, F FAN et al. SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer. IEEE/CAA Journal of Automatica Sinica, 9, 1200-1217(2022).

    Kunyu SI, Chunhui NIU. Triple multi-modal image fusion algorithm based on mixed difference convolution and efficient vision Transformer network[J]. Infrared and Laser Engineering, 2024, 53(11): 20240305
    Download Citation