Infrared and Visible Image Fusion Based on a Rolling Guidance Filter
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摘要: 为提高融合图像更加适应人类视觉感知，并解决可见光图像受光线、天气等影响而导致融合效果不佳的问题，本文提出了一种基于滚动引导滤波的可见光与红外图像融合方法。首先，利用引导滤波对可见光图像的内容进行增强，然后，利用滚动引导滤波将可见光和红外图像进行多尺度分解为小尺度层、大尺度层和基础层。在大尺度层的信息合成的过程中利用加权最小二乘法融合规则解决融合时可见光与红外图像不同特征带来的困扰，提高融合图像的视觉效果；在基础层的融合过程中采用优化的视觉显著图融合规则，减少对比度损失。最后，将大尺度层、小尺度层与基础层合并为融合后的图像。实验结果表明所给方法在提高视觉感知、细节处理、边缘保护等方面都有良好的效果。Abstract: The fusion image must be made more suitable for human visual perception and the problem of a poor fusion effect caused by light and weather must be solved. Therefore, this study proposes a fusion method of visible and infrared images based on a rolling guidance filter. First, guided filtering is used to enhance the content of the visible image. Then, a rolling guidance filter is used to decompose the visible and infrared images into small-scale, large-scale, and base layers. In the process of information synthesis of large-scale layers, the weighted least square fusion rule is used to solve the problem caused by different features of visible and infrared images, and to improve the visual effect of fusion images. In the process of fusion of the base layer, the optimized fusion rule of the visual saliency map is used to reduce the loss of contrast. Finally, the large-scale, small-scale, and base layers are merged into a fused image. The experimental results show that the proposed method improves the visual effect, detail processing, and edge protection.
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Key words:
- image fusion /
- rolling guidance filter /
- enhancement /
- weighted least square /
- visual saliency map
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图 2 基于多尺度分解的滚动方向导波融合流程图
Figure 2. Flow chart of fusion based on MSD of the rolling guided filter
表 1 用DWT、CVT、GFF、MGF、RGF_GS和RGF_GSE方法融合得到的指标
Table 1. Comparison with DWT, CVT, GFF, MGF, RGF_GS and RGF_GSE of different processing results
Criteria DWT CVT GFF MGF RGF_GS RGF_GSE EN 7.0418 7.0821 6.7096 6.6521 6.6325 7.1212 SD 42.9531 41.8259 34.9514 33.4429 36.0834 43.0536 QTE 0.3753 0.3713 0.41158 0.3804 0.3964 0.41163 QNCIE 0.80951 0.8083 0.8101 0.8059 0.8067 0.80952 PS 21.4059 20.7318 19.7443 18.4000 19.5114 24.8152 Time/s 2.03902 2.7364 1.5646 1.6532 1.9215 1.6604 
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[1] ZHANG H, MA X, TIAN Y S. An image fusion method based on Curvelet transform and guided filter enhancement[J]. Mathematical Problems in Engineering, 2020(4): 1-8(DOI: 10.1155/2020/9821715) [2] 张慧, 常莉红, 马旭, 等. 一种基于曲波变换与引导滤波增强的图像融合方法[J]. 吉林大学学报: 理学版, 2020, 58(1): 113-119. https://www.cnki.com.cn/Article/CJFDTOTAL-JLDX202001018.htmZHANG H, CHANG L H, MA X. An image fusion method based on Curvelet transform and guide filtering enhancement[J]. Journal of Jilin University: Science Edition, 2020, 58(1): 113-119. https://www.cnki.com.cn/Article/CJFDTOTAL-JLDX202001018.htm [3] 张慧, 常莉红. 基于方向导波增强的红外与可见光图像融合[J]. 激光与红外, 2020, 50(4): 508-512. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW202004021.htmZHANG H, CHANG L H. Infrared and visible image fusion based on guided filtering enhancement[J]. Laser & Infrared, 2020, 50(4): 508-512. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW202004021.htm [4] LIU Y, LIU S, WANG Z. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015, 24: 147-164. doi: 10.1016/j.inffus.2014.09.004 [5] LI Shutao, KANG Xudong, HU Jianwen. Image fusion with guided filtering[J]. IEEE Trans. Image Process, 2013, 22(7): 2864-2875. doi: 10.1109/TIP.2013.2244222 [6] Bavirisetti D P, XIAO G, ZHAO H, et al. Multi-scale guided image and video fusion: a fast and efficient approach[J]. Circuits System Signal Process, 2019, 38: 5576-5605. doi: 10.1007/s00034-019-01131-z [7] GAN W, WU X, WU W, et al. Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter[J]. Infrared Physics & Technology, 2015, 72: 37-51. [8] ZHANG Q, SHEN X, XU L, et al. Rolling guidance filter[C]//European Conference on Computer Vision, Springer, 2014: 815-830. [9] MA J, ZHOU Z, WANG B, et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization[J]. Infrared Physics & Technology, 2017, 82: 8-17. [10] ZHAI Y, Shah M. Visual attention detection in video sequences using spatiotemporal cues[C]//Proceedings of the 14th ACM International Conference on Multimedia, ACM, 2006: 815-824. [11] Stark J A. Adaptive image contrast enhancement using generalizations of histogram equalization[J]. IEEE Transactions on Image Processing, 2000, 9(5): 889-896. doi: 10.1109/83.841534 [12] Rizzi A, Gatta C, Marini D. A new algorithm for unsupervised global and local color correction[J]. Pattern Recognition Letters, 2003, 24: 1663-1677. doi: 10.1016/S0167-8655(02)00323-9 [13] ZHOU Z, WANG B, LI S, et al. Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with gaussian and bilateral filters[J]. Information Fusion, 2016, 30: 15-26. doi: 10.1016/j.inffus.2015.11.003 [14] DONG Z, LAI C, QI D, et al. A general memristor-based Pulse coupled neural network with variable linking coefficient for multi-focus image fusion[J]. Neurocomputing, 2018, 308: 172-183. doi: 10.1016/j.neucom.2018.04.066 [15] ZHOU Z, DONG M, XIE X, et al. Fusion of infrared and visible images for night-vision context enhancement[J]. Applied Optics, 2016, 55(23): 6480-6489. doi: 10.1364/AO.55.006480 [16] ZHAI Y, Shah M. Visual attention detection in video sequences using spatiotemporal cues[C]//Proceedings of the 14th ACM International Conference on Multimedia, ACM, 2006: 815-824. [17] Nava R, Cristo´bal G, Escalante-Ramırez B. Mutual information improves image fusion quality assessments[EB/OL][2007-09-04]. https://spie.org/news/0824-mutual-information-improves-image-fusion-quality-assessments?SSO=1 [18] WANG Q, SHEN Y, JIN J. Performance evaluation of image fusion techniques[J]. Image Fusion: Algorithms and Applications, 2008, 9(10): 469-492. -
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