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基于显著矩阵与神经网络的红外与可见光图像融合

Infrared and Visible Image Fusion Based on Significant Matrix and Neural Network

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摘要

针对红外与可见光图像融合过程中出现的细节损失严重、视觉效果不佳等问题,提出了基于多尺度几何变换模型的融合方法。首先,采用改进的视觉显著性检测算法对红外与可见光图像进行显著性检测,并构建显著性矩阵;然后,对红外与可见光图像进行非下采样剪切波变换,得到相应的低频和高频子带,并采用显著性矩阵对低频子带进行自适应加权融合,同时采用简化的脉冲耦合神经网络并结合多方向拉普拉斯能量和对高频子带进行融合处理;最后,通过逆变换得到融合图像。实验结果表明,该方法能够有效提升融合图像的对比度并保留源图像的细节信息,融合图像具有良好的视觉效果,且多个客观评价指标均表现良好。

Abstract

In view of the serious detail loss and poor visual effect in the process of infrared and visible image fusion, a fusion method based on the multi-scale geometric transformation model is proposed. First, the improved visual saliency detection algorithm is used to detect the significance of infrared and visible images and construct the saliency matrix. Then, the infrared and visible images are transformed by the non-subsampled shearlet transform to obtain the corresponding low-frequency and high-frequency subbands. Simultaneously, the low-frequency subbands are adaptively weighted by the saliency matrix and the high-frequency subbands are fused by the simplified pulse coupled neural network combined with the multi-direction sum-modified-Laplacian. Finally, the fusion image is obtained by inverse transformation. The experimental results show that this method can effectively improve the contrast of the fusion image and retain the details of the source image. The fusion image has a good visual effect and performs well in a variety of objective evaluation indicators.

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中图分类号:TP391

DOI:10.3788/LOP57.201007

所属栏目:图像处理

基金项目:国家自然科学基金;

收稿日期:2019-12-12

修改稿日期:2020-02-25

网络出版日期:2020-10-01

作者单位    点击查看

沈瑜:兰州交通大学电子与信息工程学院, 甘肃 兰州 730070
陈小朋:兰州交通大学电子与信息工程学院, 甘肃 兰州 730070
苑玉彬:兰州交通大学电子与信息工程学院, 甘肃 兰州 730070
王霖:兰州交通大学电子与信息工程学院, 甘肃 兰州 730070
张泓国:兰州交通大学电子与信息工程学院, 甘肃 兰州 730070

联系人作者:陈小朋(3064683191@qq.com)

备注:国家自然科学基金;

【1】Jin X, Jiang Q, Yao S W, et al. A survey of infrared and visual image fusion methods [J]. Infrared Physics & Technology. 2017, 85: 478-501.

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

【3】Li S T, Kang X D, Fang L Y, et al. Pixel-level image fusion: a survey of the state of the art [J]. Information Fusion. 2017, 33: 100-112.

【4】Guo Q M, Wang Y, Li H S. Anti-halation method of visible and infrared image fusion based on improved IHS-Curvelet transform [J]. Infrared and Laser Engineering. 2018, 47(11): 440-448.
郭全民, 王言, 李翰山. 改进IHS-Curvelet变换融合可见光与红外图像抗晕光方法 [J]. 红外与激光工程. 2018, 47(11): 440-448.

【5】Yang S Y, Wang M, Jiao L C, et al. Image fusion based on a new contourlet packet [J]. Information Fusion. 2010, 11(2): 78-84.

【6】Wang F, Cheng Y M. Improved infrared and gray visible light image fusion algorithm based on Shearlet transform [J]. Control and Decision. 2017, 32(4): 703-708.
王峰, 程咏梅. 基于Shearlet变换域改进的IR与灰度VIS图像融合算法 [J]. 控制与决策. 2017, 32(4): 703-708.

【7】Zhang Q, Maldague X. An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing [J]. Infrared Physics & Technology. 2016, 74: 11-20.

【8】Jin X, Jiang Q, Yao S W, 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.

【9】Feng X. Fusion of infrared and visible images based on Tetrolet framework [J]. Acta Photonica Sinica. 2019, 48(2): 0210001.
冯鑫. Tetrolet框架下红外与可见光图像融合 [J]. 光子学报. 2019, 48(2): 0210001.

【10】Easley G, Labate D, Lim W Q. Sparse directional image representations using the discrete shearlet transform [J]. Applied and Computational Harmonic Analysis. 2008, 25(1): 25-46.

【11】Easley G R, Labate D, Lim W Q. Optimally sparse image representations using shearlets . [C]∥2006 Fortieth Asilomar Conference on Signals, Systems and Computers, October 29-November 1, 2006, Pacific Grove, CA, USA. New York: IEEE. 2006, 974-978.

【12】Jiang Z T, Wu H, Zhou X L. Infrared and visible image fusion algorithm based on improved guided filtering and dual-channel spiking cortical model [J]. Acta Optica Sinica. 2018, 38(2): 0210002.
江泽涛, 吴辉, 周哓玲. 基于改进引导滤波和双通道脉冲发放皮层模型的红外与可见光图像融合算法 [J]. 光学学报. 2018, 38(2): 0210002.

【13】Wu Y Q, Wang Z L. Infrared and visible image fusion based on target extraction and guided filtering enhancement [J]. Acta Optica Sinica. 2017, 37(8): 0810001.
吴一全, 王志来. 基于目标提取与引导滤波增强的红外与可见光图像融合 [J]. 光学学报. 2017, 37(8): 0810001.

【14】Kong W W, Wang B H, Li B B. Image fusion multiresolution non-subsampled[M]. Xi''''an: Xidian University Press, 2015, 238-241.
孔韦韦, 王炳和, 李斌兵. 图像融合技术[M]. 西安: 西安电子科技大学出版社, 2015, 238-241.

【15】He K M, Sun J, Tang X O. Guided image filtering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013, 35(6): 1397-1409.

【16】Achanta R, Hemami S, Estrada F, et al. Frequency-tuned salient region detection . [C]∥2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25,2009, Miami, FL, USA. New York: IEEE. 2009, 1597-1604.

【17】Wang Y L. Research on the medical image fusion algorithm based on NSST Xi''''an: [D]. Xidian University. 2018.
王彦龙. 基于NSST的医学图像融合算法研究 [D]. 西安: 西安电子科技大学. 2018.

【18】Huang W, Jing Z L. Evaluation of focus measures in multi-focus image fusion [J]. Pattern Recognition Letters. 2007, 28(4): 493-500.

【19】Shreyamsha Kumar B K. Image fusion based on pixel significance using cross bilateral filter [J]. Signal, Image and Video Processing. 2015, 9(5): 1193-1204.

【20】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.

【21】Zhang Q H, Fu Y L, Li H F, et al. Dictionary learning method for joint sparse representation-based image fusion [J]. Optical Engineering. 2013, 52(5): 057006.

【22】Liu C H, Qi Y, Ding W R. Infrared and visible image fusion method based on saliency detection in sparse domain [J]. Infrared Physics & Technology. 2017, 83: 94-102.

引用该论文

Shen Yu,Chen Xiaopeng,Yuan Yubin,Wang Lin,Zhang Hongguo. Infrared and Visible Image Fusion Based on Significant Matrix and Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201007

沈瑜,陈小朋,苑玉彬,王霖,张泓国. 基于显著矩阵与神经网络的红外与可见光图像融合[J]. 激光与光电子学进展, 2020, 57(20): 201007

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