中文

Journal of Applied Sciences >

2022 , Vol. 40 >Issue 1: 80 - 92

DOI: https://doi.org/10.3969/j.issn.0255-8297.2022.01.008

Special Issue on Computer Applications

Infrared Image Fusion Based on NSCT and Compressed Sensing

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  • 1. School of Information Engineering, East China University of Technology, Nanchang 330013, Jiangxi, China;
    2. Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, Jiangxi, China

Received date: 2021-07-12

  Online published: 2022-01-28

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Abstract

Aiming at the problems of low quality, lack of information and non-prominent edge details in the fusion process of infrared and visible images, this paper proposes a compressed sensing image fusion and reconstruction algorithm based on non-subsampled contourlet transform (NSCT) and sparse representation. Firstly, a source image is decomposed by using NSCT to obtain corresponding high-frequency sub-band and low-frequency sub-band images. Then, the high-frequency sub-band images are fused by using the highfrequency fusion rules based on compressed sensing to obtain high-frequency fusion coefficients. For the low-frequency sub-band images, low-frequency fusion coefficients are obtained by using the low-frequency fusion rules based on dictionary learning. Finally, a fusion image is obtained by using the inverse NSCT transformation to achieve superresolution recovery of infrared and visible images. Experimental results show that the images fused by this algorithm have good performance in metrics, such as average gradient, edge intensity, information entropy, edge information retention and spatial frequency, and prove that this fusion algorithm has significant advantages in image fusion quality.

Key words： image fusion; non-subsampled contour transform (NSCT); sparse representation; compressed sensing (CS); infrared image

Cite this article

JIN An'an, LI Xiang, ZHANG Li, XIONG Qingzhi . Infrared Image Fusion Based on NSCT and Compressed Sensing[J]. Journal of Applied Sciences, 2022 , 40(1) : 80 -92 . DOI: 10.3969/j.issn.0255-8297.2022.01.008

References

[1] Kaur H, Koundal D, Kadyan V. Image fusion techniques:a survey[J]. Archives of Computational Methods in Engineering, 2021:1-23.
[2] 陈清江, 李毅, 柴昱洲. 基于卷积神经网络的红外图像融合算法[J]. 激光与红外, 2019, 49(1):123-128. Chen Q J, Li Y, Chai Y Z. Infrared image fusion algorithm based on convolutional neural network[J]. Laser & Infrared, 2019, 49(1):123-128. (in Chinese)
[3] 甄媚. 可见光图像与红外图像融合算法研究[D]. 西安:西安科技大学, 2019.
[4] 冯颖, 贺兴时, 薛菁菁, 等. 基于NSCT的SAR与可见光图像融合算法[J]. 电光与控制, 2018, 25(3):23-27. Feng Y, He X S, Xue J J, et al. Fusion algorithm of SAR and visible image based on NSCT[J]. Electro-optical and Control, 2018, 25(3):23-27. (in Chinese)
[5] 罗高鹏. 基于多尺度变换的红外与可见光图像融合算法研究[D]. 合肥:合肥工业大学, 2017.
[6] 丁贵鹏, 陶钢, 李英超, 等. 基于非下采样轮廓波变换与引导滤波器的红外及可见光图像融合[J]. 兵工学报, 2021, 42(9):1911-1922. Ding G P, Tao G, Li Y C, et al. Infrared and visible light image fusion based on nonsubsampled contourlet transform and guided filter[J]. Acta Armamentarii, 2021, 42(9):1911-1922. (in Chinese)
[7] 杨孙运, 奚峥皓, 王汉东, 等. 基于NSCT和最小化-局部平均梯度的图像融合[J]. 红外技术, 2021, 43(1):13-20. Yang S Y, Xi Z H, Wang H D, et al. Image fusion based on NSCT and minimum-local mean gradient[J]. Infrared Technology, 2021, 43(1):13-20. (in Chinese)
[8] Yang Y, Ding M, Huang S, et al. Multi-focus image fusion via clustering PCA based joint dictionary learning[J]. IEEE Access, 2017, 5:16985-16997.
[9] Xia J, Chen Y, Chen A. Medical image fusion based on sparse representation and PCNN in NSCT domain[J]. Computational and Mathematical Methods in Medicine, 2018, 2018:1-12.
[10] Bhateja V, Patel H, Krishn A, et al. Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transform domains[J]. IEEE Sensors Journal, 2015, 15(12):6783-6790.
[11] Zhang Q, Guo B L. Research on image fusion based on the nonsubsampled contourlet transform[C]//2007 IEEE International Conference on Control and Automation, 2007:3239-3243.
[12] Da Cunha A L, Zhou J, Do M N. The nonsubsampled contourlet transform:theory, design, and applications[C]//IEEE Transactions on Image Processing, 2006, 15(10):3089-3101.
[13] Wang Z, Li X, Duan H, et al. Medical image fusion based on convolutional neural networks and non-subsampled contourlet transform[J]. Expert Systems with Applications, 2021, 171:114574.
[14] Li X, Zhao J. A novel multi-modal medical image fusion algorithm[J]. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(2):1995-2002.
[15] Olshausen B A, Field D J. Sparse coding with an overcomplete basis set:a strategy employed by V1?[J]. Vision Research, 1997, 37(23):3311-3325.
[16] Li Q, Wang W, Chen G, et al. Medical image fusion using segment graph filter and sparse representation[J]. Computers in Biology and Medicine, 2021, 131:104239.
[17] Maqsood S, Javed U. Multi-modal medical image fusion based on two-scale image decomposition and sparse representation[J]. Biomedical Signal Processing and Control, 2020, 57:101810.
[18] 余汪洋, 陈祥光, 董守龙, 等. 基于小波变换的图像融合算法研究[J]. 北京理工大学学报, 2014, 34(12):1262-1266. Yu W Y, Chen X G, Dong S L, et al. Research on image fusion algorithm based on wavelet transform[J]. Transactions of Beijing Institute of Technology, 2014, 34(12):1262-1266. (in Chinese)
[19] Jin X, Jiang Q, Yao S, et al. Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain[J]. Infrared Physics & Technology, 2018, 88:1-12.
[20] Liu Y, Chen X, Peng H, et al. Multi-focus image fusion with a deep convolutional neural network[J]. Information Fusion, 2017, 36:191-207.
[21] Liu Y, Chen X, Ward R K, et al. Medical image fusion via convolutional sparsity based morphological component analysis[J]. IEEE Signal Processing Letters, 2019, 26(3):485-489.
[22] Liu Y, Chen X, Ward R K, et al. Image fusion with convolutional sparse representation[J]. IEEE Signal Processing Letters, 2016, 23(12):1882-1886.
[23] Liu Y, Liu S, Wang Z. Multi-focus image fusion with dense SIFT[J]. Information Fusion, 2015, 23:139-155.
[24] Naji Ma, Aghagolzadeh A. Multi-focus image fusion in DCT domain based on correlation coefficient[C]//20152nd International Conference on Knowledge-Based Engineering and Innovation, 2015:632-639.
[25] Liang W, Long J, Li K C, et al. A fast defogging image recognition algorithm based on bilateral hybrid filtering[C]//ACM Transactions on Multimedia Computing, Communications, and Applications, 2021, 17(2):1-16.
[26] Tamilkodi R, Nesakumari G R. A novel framework for retrieval of image using weighted edge matching algorithm[J]. Multimedia Tools and Applications, 2021, 80(13):19625-19648.
[27] Jeon G. Information entropy algorithms for image, video, and signal processing[J]. Entropy, 2021, 23(8):926.
[28] Li X, Zhao J. A novel multi-modal medical image fusion algorithm[J]. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(2):1995-2002.
[29] Liu C, Yang B, Li Y, et al. An information retention and feature transmission network for infrared and visible image fusion[J]. IEEE Sensors Journal, 2021, 21(13):14950-14959.
[30] Jian L, Rayhana R, Ma L, et al. Infrared and visible image fusion based on deep decomposition network and saliency analysis[J]. IEEE Transactions on Multimedia, 2021, 7(9):1-13.
[31] Ma C, Wang R, Zhou S, et al. The structural similarity index for IMRT quality assurance:radiomics-based error classification[J]. Medical Physics, 2021, 48(1):80-93.
[32] Zhang W, Wu Q J, Yang Y, et al. Fast ship detection with spatial-frequency analysis and ANOVA-based feature fusion[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19:1-5.
[33] Kanwal N, Jain S, Kaur P. Evaluating robustness for intensity based image registration measures using mutual information[J]. Cognitive Computing in Human Cognition:Perspectives and Applications, 2020, 17:73.
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