• Laser & Optoelectronics Progress
  • Vol. 61, Issue 20, 2011011 (2024)
Yihang Cheng1,2,*, Zhengyu Qiao1,3, Yong Huang1,2,3, and Qun Hao1,2,3
Author Affiliations
  • 1School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
  • 2Yangtze Delta Region Academy, Beijing Institute of Technology, Jiaxing 314003, Zhejiang , China
  • 3National Key Laboratory on Near-Surface Detection, Beijing 100087, China
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    DOI: 10.3788/LOP241637 Cite this Article Set citation alerts
    Yihang Cheng, Zhengyu Qiao, Yong Huang, Qun Hao. Luminance-Adaptive Infrared and Visible Image Fusion Based on Retinex Theory (Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(20): 2011011 Copy Citation Text show less
    References

    [1] Sun N, Qin H M, Zhang L et al. Vehicle target recognition based on multi-sensor information fusion[J]. Automotive Engineering, 39, 1310-1315(2017).

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

    [3] Si T B, Jia F X, Lü Z Q et al. Infrared and visible image fusion based on gradient domain-guided filtering and significance analysis[J]. Laser & Optoelectronics Progress, 61, 0837010(2024).

    [4] Wu Y F, Yang R, Lü Q S et al. Infrared and visible image fusion: statistical analysis, deep learning approaches and future prospects[J]. Laser & Optoelectronics Progress, 61, 1400004(2024).

    [5] Ning D H, Zheng S. An object detection algorithm based on decision-level fusion of visible and infrared images[J]. Infrared Technology, 45, 282-291(2023).

    [6] Yan H Z. Research on multi-source visual information fusion for night autonomous driving[D], 19-25(2023).

    [7] Wang P. Design and implementation of wearable optic-infrared fusion imaging monitoring system[D], 16-27(2020).

    [8] Song Z H. Research on forest fire smoke detection based on visible and infrared remote sensing images[D], 15-20(2023).

    [9] Xing Q B. Research on SAR and visible image fusion algorithm based on deep learning[D], 23-27(2023).

    [10] Li S T, Yin H T, Fang L Y. Group-sparse representation with dictionary learning for medical image denoising and fusion[J]. IEEE Transactions on Bio-Medical Engineering, 59, 3450-3459(2012).

    [11] Li S T, Yang B, Hu J W. Performance comparison of different multi-resolution transforms for image fusion[J]. Information Fusion, 12, 74-84(2011).

    [12] 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).

    [13] Bavirisetti D P, Xiao G, Liu G. Multi-sensor image fusion based on fourth order partial differential equations[C], 1-9(2017).

    [14] Zhang X Y, Ma Y, Fan F et al. Infrared and visible image fusion via saliency analysis and local edge-preserving multi-scale decomposition[J]. Journal of the Optical Society of America A, 34, 1400-1410(2017).

    [15] Ma J L, Zhou Z Q, Wang B et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization[J]. Infrared Physics & Technology, 82, 8-17(2017).

    [16] Li H, Wu X J. DenseFuse: a fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 28, 2614-2623(2019).

    [17] 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).

    [18] Ma J Y, Tang L F, Xu M L et al. STDFusionNet: an infrared and visible image fusion network based on salient target detection[J]. IEEE Transactions on Instrumentation and Measurement, 70, 5009513(2021).

    [19] Land E H, McCann J J. Lightness and retinex theory[J]. Journal of the Optical Society of America A, 61, 1-11(1971).

    [20] Bigun J, Granlund G H. Optimal orientation detection of linear symmetry[EB/OL]. https:∥www.researchgate.net/publication/284108111_Optimal_orientation_detection_of_linear_symmetry

    [21] Shao Y, Sun F C, Liu Y. A no-reference image quality assessment method using local structure tensor[J]. Journal of Electronics & Information Technology, 34, 1779-1785(2012).

    [22] Smith A R. Color gamut transform pairs[J]. ACM Siggraph Computer Graphics, 12, 12-19(1978).

    [23] Pang C, Chen Y Z, Wang L et al. Different attentional selection modes of object information in the encoding and maintenance stages of visual working memory[J]. Acta Psychologica Sinica, 55, 1397-1410(2023).

    [24] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

    [25] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision[C], 2818-2826(2016).

    [26] Zhang F, Shao Y J, Sun Y S et al. Unsupervised low-light image enhancement via histogram equalization prior[EB/OL]. https://arxiv.org/abs/2112.01766v1

    [27] Tang L F, Xiang X Y, Zhang H et al. DIVFusion: darkness-free infrared and visible image fusion[J]. Information Fusion, 91, 477-493(2023).

    [28] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556v6

    [29] Reza A M. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement[J]. Journal of VLSI Signal Processing Systems for Signal, 38, 35-44(2004).

    [30] Zhang Y H, Zhang J W, Guo X J. Kindling the darkness: a practical low-light image enhancer[C], 1632-1640(2019).

    [31] Jia X Y, Zhu C, Li M Z et al. LLVIP: a visible-infrared paired dataset for low-light vision[C], 3489-3497(2021).

    [32] 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).

    [33] Ma J Y, Yu W, Liang P W et al. FusionGAN: a generative adversarial network for infrared and visible image fusion[J]. Information Fusion, 48, 11-26(2019).

    [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] Li S T, Kang X D, Hu J W. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing, 22, 2864-2875(2013).

    [36] Sheikh H R, Bovik A C. Image information and visual quality[J]. IEEE Transactions on Image Processing, 15, 430-444(2006).

    [37] Xu H, Ma J Y, Le Z L et al. FusionDN: a unified densely connected network for image fusion[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12484-12491(2020).