A survey of infrared and visual image fusion methods

Infrared Physics and Technology

Volumn 85, Issue , 2017, Pages 478-501

  Jin, Xin ^a   Jiang, Qian ^a   Yao, Shaowen ^a   Zhou, Dongming ^a   Nie, Rencan ^a   Hai, Jinjin ^b   He, Kangjian ^a  

^a YUNNAN UNIVERSITY   (China)

^b NATIONAL DIGITAL SWITCHING SYSTEM ENGINEERING AND TECHNOLOGICAL RESEARCH CENTER   (China)

Author keywords

Feature extraction; Image fusion; Image quality assessment; Image representation; Infrared and visual image

Indexed keywords

FEATURE EXTRACTION; IMAGE QUALITY; SURVEYS;

IMAGE FUSION METHODS; IMAGE QUALITY ASSESSMENT; IMAGE REPRESENTATIONS; IMAGING EQUIPMENT; POTENTIAL RESEARCHES; REDUNDANCY INFORMATION; VISUAL ABILITIES; VISUAL IMAGE;

IMAGE FUSION;

EID: 85028588752     PISSN: 13504495     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.infrared.2017.07.010     Document Type: Review

References (177)

1. Baohua, Zhang, Xiaoqi, Lu, Haiquan, Pei, Ying, Zhao, A fusion algorithm for infrared and visible images based on saliency analysis and non-subsampled Shearlet transform. Infrared Phys. Technol. 73 (2015), 286–297.
2. Jin, Haiyan, Wang, Yanyan, A fusion method for visible and infrared images based on contrast pyramid with teaching learning based optimization. Infrared Phys. Technol. 64 (2014), 134–142.
3. Liu, Zhaodong, Yin, Hongpeng, Fang, Bin, Chai, Yi, A novel fusion scheme for visible and infrared images based on compressive sensing. Optics Commun. 335 (2015), 168–177.
4. Li, Shutao, Kang, Xudong, Fang, Leyuan, Hub, Jianwen, Yin, Haitao, Pixel-level image fusion: a survey of the state of the art. Inform. Fusion 33 (2017), 100–112.
5. Cui, Guangmang, Feng, Huajun, Xu, Zhihai, Li, Qi, Chen, Yueting, Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition. Opt. Commun. 341 (2015), 199–209.
6. Fan, Xinnan, Shi, Pengfei, Ni, Jianjun, Li, Min, A thermal infrared and visible images fusion based approach for multitarget detection under complex environment. Math. Problems Eng., 2015, 2015 750708:1–750708:11.
7. Bhatnagar, G., Liu, Z., A novel image fusion framework for night-vision navigation and surveillance. SIViP 9:1 (2015), 165–175.
8. James, Alex Pappachen, Dasarathy, Belur V., Medical image fusion: a survey of the state of the art. Inform. Fusion 19 (2014), 4–19.
9. Muller, Amanda C., Narayanan, S., Cognitively-engineered multisensor image fusion for military applications. Inform. Fusion 10 (2009), 137–149.
10. Feng, Z.J., Zhang, X.L., Yuan, L.Y., et al. Infrared target detection and location for visual surveillance using fusion scheme of visible and infrared images. Math. Problems Eng. 3 (2013), 831–842.
11. Elguebaly, Tarek, Bouguil, Nizar, Finite asymmetric generalized Gaussian mixture models learning for infrared object detection. Comput. Vis. Image Underst. 117 (2013), 1659–1671.
12. Mendozaa, Fernando, Lu, Renfu, Cen, Haiyan, Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content. Postharvest Biol. Technol. 73 (2012), 89–98.
13. Bulanon, D.M., Burks, T.F., Alchanatis, V., Image fusion of visible and thermal images for fruit detection. Biosys. Eng. 103:1 (2009), 12–22.
14. Raza, Shan-e-Ahmed, Sanchez, Victor, Prince, Gillian, Clarkson, John P., Rajpoot, Nasir M., Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain. Pattern Recogn. 48 (2015), 2119–2128.
15. Apatean, Anca, Rogozan, Alexandrina, Bensrhair, Abdelaziz, Visible-infrared fusion schemes for road obstacle classification. Transp. Res. Part C 35 (2013), 180–192.
16. Eisler, K., Homma, C., Goldammer, M., Rothenfusser, M., Arnold, W., Fusion of visual and infrared thermography images for advanced assessment in nondestructive testing. Rev. Sci. Instrum., 84, 2013 064902:1-5.
17. Wong, W.K., Zhao, Haitao, Eyeglasses removal of thermal image based on visible information. Inform. Fusion 14 (2013), 163–176.
18. Abaza, Ayman, Bourlai, Thirimachos, On ear-based human identification in the mid-wave infrared spectrum. Image Vis. Comput. 31 (2013), 640–648.
19. González, A., Fang, Z., Socarras, Y., et al. Pedestrian detection at Day/Night time with visible and FIR cameras: a comparison. 820 Sensors, 16(6), 2016 :1-820:11.
20. Uzairn, Muhammad, Mahmood, Arif, Mian, Ajmal, McDonald, Chris, Periocular region-based person identification in the visible, infrared and hyperspectral imager. Neurocomputing 149 (2015), 854–867.
21. Dixon, T.D., Nikolov, S.G., Lewis, J.J., et al. Task-based scanpath assessment of multi-sensor video fusion in complex scenarios. Inform. Fusion 11:1 (2010), 51–65.
22. Talha, Mohammed, Stolkin, Rustam, Particle filter tracking of camouflaged targets by adaptive fusion of thermal and visible spectra camera data. IEEE Sens. J. 14:1 (2014), 159–166.
23. Kong, Seong G., Heo, Jingu, Abidi, Besma R., Paik, Joonki, Abidi, Mongi A., Recent advances in visual and infrared face recognition-a review. Comput. Vis. Image Underst. 97 (2005), 103–135.
24. Bebis, George, Gyaourova, Aglika, Singh, Saurabh, Pavlidis, Ioannis, Face recognition by fusing thermal infrared and visible imagery. Image Vis. Comput. 24 (2006), 727–742.
25. Hizem, W., Allano, L., Mellakh, A., Dorizzi, B., Face recognition from synchronised visible and near-infrared images. IET Signal Proc. 3:4 (2009), 282–288.
26. Shamsafar, Faranak, Seyedarabi, Hadi, Aghagolzadeh, Ali, Fusing the information in visible light and near-infrared images for iris recognition. Mach. Vis. Appl. 25 (2014), 881–899.
27. Hermosilla, G., Gallardo, F., Farias, G., et al. Fusion of visible and thermal descriptors using genetic algorithms for face recognition systems. Sensors 15:8 (2015), 17944–17962.
28. Bai, Junfeng, Ma, Yong, Li, Jing, Li, Hao, Fang, Yu, Wang, Rui, Wang, Hongyuan, Good match exploration for thermal infrared face recognition based on YWF-SIFT with multi-scale fusion. Infrared Phys. Technol. 67 (2014), 91–97.
29. Yan, X., Qin, H., Li, J., et al. Infrared and visible image fusion with spectral graph wavelet transform. J. Opt. Soc. Am. A 32:9 (2015), 1643–1652.
30. Hu, Jianwen, Li, Shutao, The multiscale directional bilateral filter and its application to multisensory image fusion. Inform. Fusion 13 (2012), 196–206.
31. Aran, Amit, Munshi, Soumika, Beri, Vinod K., Gupta, Arun K., Spectral band invariant wavelet-modified MACH filter. Opt. Lasers Eng. 46 (2008), 656–665.
32. Singh, Richa, Vatsa, Mayank, Noore, Afzel, Hierarchical fusion of multi-spectral face images for improved recognition performance. Inform. Fusion 9 (2008), 200–210.
33. Singh, Richa, Vatsa, Mayank, Noore, Afzel, Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition. Pattern Recogn. 41 (2008), 880–893.
34. Ma, Jiayi, Zhao, Ji, Ma, Yong, Tian, Jinwen, Non-rigid visible and infrared face registration via regularized Gaussian fields criterio. Pattern Recogn. 48 (2015), 772–784.
35. Raghavendra, R., Dorizzi, Bernadette, Rao, Ashok, Hemantha Kumar, G., Particle swarm optimization based fusion of near infrared and visible images for improved face verification. Pattern Recogn. 44 (2011), 401–411.
36. Hanna, B.V., Gorbach, A.M., Gage, F.A., et al. Intraoperative assessment of critical biliary structures with visible range/infrared image fusion. J. Am. Coll. Surg. 206:3 (2008), 1227–1231.
37. Eslami, Mehrdad, Mohammadzadeh, Ali, Developing a spectral-based strategy for urban object detection from airborne hyperspectral TIR and visible data. IEEE J. Select. Top. Appl. Earth Observat. Remote Sens. 9:5 (2016), 1808–1816.
38. Lach, Stephen R., Kerekes, John P., Fan, Xiaofeng, Fusion of multiple image types for the creation of radiometrically-accurate synthetic scenes. J. Appl. Remote Sens., 3, 2009, 033501.
39. Han, Lei, Wulie, Buzha, Yang, Yiling, Wang, Hongqing, Direct fusion of geostationary meteorological satellite visible and infrared images based on thermal physical properties. Sensors 15:1 (2015), 703–714.
40. Chang, X., Jiao, L., Liu, F., et al. Multicontourlet-based adaptive fusion of infrared and visible remote sensing images. IEEE Geosci. Remote Sens. Lett. 7:3 (2010), 549–553.
41. Qingping, D.J.L., Liang, X.U., Visible and infrared video fusion using uniform discrete curvelet transform and spatial-temp oral information. Chin. J. Electron. 24:4 (2015), 761–766.
42. Ren, Kan, Xu, Fuyuan, Super-resolution images fusion via compressed sensing and low-rank matrix decomposition. Infrared Phys. Technol. 68 (2015), 61–68.
43. Lu, X., Zhang, J., Li, T., et al. Synergetic classification of long-wave infrared hyperspectral and visible images. IEEE J. Select. Top. Appl. Earth Observat. Remote Sens. 8:7 (2015), 3546–3557.
44. Han, L., Shi, L., Yang, Y., et al. Thermal physical property-based fusion of geostationary meteorological satellite visible and infrared channel images. Sensors 14:6 (2014), 10187–10202.
45. Piella, Gemma, A general framework for multiresolution image fusion: from pixels to regions. Inform. Fusion 4 (2003), 259–280.
46. Wang, P.S.P., Yang, Jianwei, A review of wavelet-based edge detection methods. Int. J. Pattern Recognit Artif Intell. 26:7 (2013), 965–1004.
47. Ahmad, Attiq, Riaz, Muhammad Mohsin, Ghafoor, Abdul, Zaidi, Tahir, An Improved Infrared/Visible Fusion for Astronomical Images. Advances in Astronomy, 2015, 2015 203872:1–8.
48. Gargano, Marco, Bertani, Duilio, Greco, Marinella, Cupitt, John, Gadia, Davide, Rizzi, Alessandro, A perceptual approach to the fusion of visible and NIR images in the examination of ancient documents. J. Cult. Heritage 16:4 (2014), S15–S17.
49. Joo Kim, Seon, Deng, Fanbo, Brown, Michael S., Visual enhancement of old documents with hyperspectral imaging. Pattern Recogn. 44 (2011), 1461–1469.
50. Lagüela, S., Armesto, J., Arias, P., Herráez, J., Automation of thermographic 3D modelling through image fusion and image matching techniques. Autom. Construct. 27 (2012), 24–31.
51. Kumar, M., Dass, S., A total variation-based algorithm for pixel-level image fusion. IEEE Trans. Image Process. 18:9 (2009), 2137–2143.
52. Petrović, Valdimir, S., Xydeas, Costas, S., Gradient-based multiresolution image fusion. IEEE Trans. Image Process. 13:2 (2004), 228–237.
53. Xu, Liang, Du, Junping, Zhang, Zhenhong, Infrared-visible video fusion based on motion-compensated wavelet transforms. Iet Image Process. 9:4 (2014), 318–328.
54. Denman, Simon, Lamb, Todd, Fookes, Clinton, Chandran, Vinod, Sridharan, Sridha, Multi-spectral fusion for surveillance systems. Comput. Electr. Eng. 36 (2010), 643–663.
55. Chan, A.L., Fusing concurrent visible and infrared videos for improved tracking performance. Opt. Eng. 52:1 (2013), 177–182.
56. Zhang, X.W., Zhang, Y.N., Guo, Z., et al. Advances and perspective on motion detection fusion in visual and thermal framework. J. Infrar. Millimeter Waves 30:4 (2012), 354–360.
57. Zhou, Yi, Mayyas, Abedalroof, Qattawi, Ala, Omar, Mohammed, Feature-level and pixel-level fusion routines when coupled to infrared night-vision tracking scheme. Infrar. Phys. Technol. 53 (2010), 43–49.
58. Liu, Congyi, Jing, Zhongliang, Xiao, Gang, Yang, Bo, Feature-based fusion of infrared and visible dynamic images using target detection. Chin. Opt. Lett. 5:5 (2007), 274–277.
59. Bennett, E.P., Mason, J.L., Mcmillan, L., Multispectral bilateral video fusion. IEEE Transact. Image Process. Publ. IEEE Signal Process. Soc. 16:5 (2007), 1185–1194.
60. He, W., Feng, W., Peng, Y., et al. Multi-level image fusion and enhancement for target detection. Optik-International Journal for Light and Electron Optics 126:11–12 (2015), 1203–1208.
61. Toet, A., Natural colour mapping for multiband nightvision imagery. Inform. Fusion 4:3 (2003), 155–166.
62. Tsagaris, V., Anastassopoulos, V., Fusion of visible and infrared imagery for night color vision. Displays 26:4–5 (2005), 191–196.
63. Toet, A., Hogervorst, M.A., Progress in color night vision. Opt. Eng., 51(1), 2012, 010901.
64. Davis, J.W., Sharma, V., Background-subtraction using contour-based fusion of thermal and visible imagery. Comput. Vis. Image Underst. 106:2–3 (2007), 162–182.
65. Z. Cheng, Q. Yang, B. Sheng, Deep Colorization, 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015, pp. 415–423.
66. Toet, A., Franken, E.M., Perceptual evaluation of different image fusion schemes. Displays 24:1 (2003), 25–37.
67. Yuan, Y., Zhang, J., Chang, B., Objective quality evaluation of visible and infrared color fusion image. Opt. Eng. 50:3 (2011), 103–108.
68. Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P., Color transfer between images. IEEE Comput. Graphics Appl. 21:5 (2001), 34–41.
69. Yin, S., Cao, L., Ling, Y., et al. One color contrast enhanced infrared and visible image fusion method. Infrared Phys. Technol. 53:2 (2010), 146–150.
70. Hogervorst, M.A., Toet, A., Fast natural color mapping for night-time imagery. Inform. Fusion 11:2 (2010), 69–77.
71. M.A. Hogervorst, A. Toet, F.L. Kooi, TNO Defense Security and Safety, Method and System for Converting at Least one First-spectrum Image into a Second-spectrum Image, 2006.
72. Zheng, Y., Essock, E.A., A local-coloring method for night-vision colorization utilizing image analysis and fusion. Inform. Fusion 9:2 (2008), 186–199.
73. Levinera, Marom, Maltz, Masha, A new multi-spectral feature level image fusion method for human interpretation. Infrared Phys. Technol. 52 (2009), 79–88.
74. Qian, X., Wang, Y., Wang, B., Effective contrast enhancement method for color night vision. Infrared Phys. Technol. 55:1 (2012), 130–136.
75. Qian, X., Wang, Y., Wang, B., Fast color contrast enhancement method for color night vision. Infrared Phys. Technol. 55:1 (2012), 122–129.
76. Guo, Shaoshu, Jin, W., Wang, L., Objective color harmony assessment for visible and infrared color fusion images of typical scenes. Opt. Eng. 51:11 (2012), 1371–1379.
77. Yu, X., Ren, J., Chen, Q., et al. A false color image fusion method based on multi-resolution color transfer in normalization YCBCR space. Optik-Int. J. Light Electron Opt. 125:20 (2014), 6010–6016.
78. R.S. Wang, T. Cai, Symbol-based image fusion for object recognition, in: Proceedings of the International Conference on Multisource-Multisensor Information Fusion (FUSION 98), 1998, pp. 509–514.
79. V.E. Neagoe, A.D. Ropot, A.C. Mugioiu, Real time face recognition using decision fusion of neural classifiers in the visible and thermal infrared spectrum, Advanced Video and Signal Based Surveillance, 2007. AVSS 2007. IEEE Conference on. IEEE, 2007, pp. 301–306.
80. Y.F. Zhao, Y.X. Yin, Decision-level fusion of infrared and visible images for face recognition, Control & Decision Conference IEEE, 2008, pp. 2411–2414.
81. S. Yeom, Multi-frame decision level fusion for face classification based on a photon-counting linear discriminant analysis, Society of Photo-Optical Instrumentation Engineers, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 2011.
82. Aili Wang, Jinna Jiang, Haoye Zhang, Multi-sensor Image Decision Level Fusion Detection Algorithm Based On D-S Evidence Theory, 2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control, IEEE Conference on. IEEE, 2014, pp. 620–623.
83. Cunha, A.L., Zhou, J., Do, M.N., The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans. Image Process 15:10 (2006), 3089–3101.
84. Zhao, Chunhui, Guo, Yunting, Wang, Yulei, A fast fusion scheme for infrared and visible light images in NSCT domain. Infrared Phys. Technol. 72 (2015), 266–275.
85. Ghassemian, Hassan, A review of remote sensing image fusion methods. Inform. Fusion 32 (2016), 75–89.
86. Romberg, J., Imaging via compressive sampling. Signal Process. Magazine IEEE 25:2 (2008), 14–20.
87. Li, X., Qin, S.Y., Efficient fusion for infrared and visible images based on compressive sensing principle. IET Image Proc. 5:2 (2011), 141–147.
88. Jameel, Amina, Ghafoor, Abdul, Riaz, Muhammad Mohsin, Adaptive compressive fusion for visible/IR sensors. IEEE Sens. J. 14:7 (2014), 2230–2231.
89. Ding, Meng, Wei, Li, Wang, Bangfeng, Research on fusion method for infrared and visible images via compressive sensing. Infrared Phys. Technol. 57 (2013), 56–67.
90. Jin, X., Nie, R., Zhou, D., Yao, S., Chen, Y., Yu, J., et al. A novel DNA sequence similarity calculation based on simplified pulse-coupled neural network and Huffman coding. Phys. A Stat. Mech. Its Appl. 461 (2016), 325–338.
91. Jin, X., Nie, R., Zhou, D., et al. Multifocus color image fusion based on NSST and PCNN. J. Sens., 2016 8359602:1-8359602:12.
92. Jin, X., Zhou, D., Yao, S., et al. Remote sensing image fusion method in CIELab color space using nonsubsampled shearlet transform and pulse coupled neural networks. J. Appl. Remote Sens., 10(2), 2016 025023:1-025023:18.
93. Kong, Weiwei, Lei, Yang, Zhao, Huaixun, Adaptive fusion method of visible light and infrared images based on non-subsampled shearlet transform and fast non-negative matrix factorization. Infrared Phys. Technol. 67 (2014), 161–172.
94. Easley, G., Labate, D., Lim, W.Q., Sparse directional image representation using the discrete shearlet transforms. Appl. Comput. Harm. Anal. 25:1 (2008), 25–46.
95. Kong, Weiwei, Zhang, Longjun, Lei, Yang, Novel fusion method for visible light and infrared images based on NSST-SF-PCNN. Infrared Phys. Technol. 65 (2014), 103–112.
96. Kong, Weiwei, Liu, Jianping, Technique for image fusion based on NSST domain improved fast non-classical RF. Infrared Phys. Technol. 61 (2013), 27–36.
97. Kong, Weiwei, Technique for gray-scale visual light and infrared image fusion based on non-subsampled shearlet transform. Infrared Phys. Technol. 63 (2014), 110–118.
98. Kong, Weiwei, Wang, Binghe, Lei, Yang, Technique for infrared and visible image fusion based on non-subsampled shearlet transform and spiking cortical model. Infrared Phys. Technol. 71 (2015), 87–98.
99. Sun, Jian, Zhu, Hongyan, Xu, Zongben, Han, Chongzhao, Poisson image fusion based on Markov random field fusion model. Inform. Fusion 14 (2013), 241–254.
100. Shibata, Takashi, Tanaka, Masayuki, Okutomi, Masatoshi, Versatile visible and near-infrared image fusion based on high visibility area selection. J. Electron. Imaging, 25(1), 2016, 013016.
101. Han, Jungong, Pauwels, Eric J., de Zeeuw, Paul, Fast saliency-aware multi-modality image fusion. Neurocomputing 111 (2013), 70–80.
102. Michael, G., Thomason, Jens Gregor, Higher order singular value decomposition of tensors for fusion of registered images. J. Electron. Imaging, 20(1), 2011, 013023.
103. Niu, Yifeng, Xu, Shengtao, Wu, Lizhen, Hu, Weidong, Airborne infrared and visible image fusion for target perception based on target region segmentation and discrete wavelet transform. Math. Problems Eng. 275138 (2012), 1–10.
104. Li, He, Liu, Lei, Huang, Wei, Yue, Chao, An improved fusion algorithm for infrared and visible images based on multi-scale transform. Infrared Phys. Technol. 74 (2016), 28–37.
105. Saeedi, Jamal, Faez, Karim, Infrared and visible image fusion using fuzzy logic and population-based optimization. Appl. Soft Comput. 12 (2012), 1041–1054.
106. Jin, Haiyan, Jiao, Licheng, Liu, Fang, Qi, Yutao, Fusion of infrared and visual images based on contrast pyramid directional filter banks using clonal selection optimizing. Opt. Eng., 47(2), 2008, 027002.
107. Jin, Haiyan, Xi, Qian, Wang, Yanyan, Hei, Xinhong, Fusion of visible and infrared images using multiobjective evolutionary algorithm based on decomposition. Infrar. Phys. Technol. 71 (2015), 151–158.
108. Xu, H., Wang, Y., Wu, Y., et al. Infrared and multi-type images fusion algorithm based on contrast pyramid transform. Infrared Phys. Technol. 78 (2016), 133–146.
109. Liu, Z., Tsukada, K., Hanasaki, K., et al. Image fusion by using steerable pyramid. Pattern Recogn. Lett. 22:9 (2001), 929–939.
110. Niu, Y., Wang, B., Hyperspectral Target Detection Using Learned Dictionary. IEEE Geosci. Remote Sens. Lett. 12:7 (2015), 1–5.
111. Wang, Jun, Peng, Jinye, Feng, Xiaoyi, He, Guiqing, Fan, Jianping, Fusion method for infrared and visible images by using non-negative sparse representation. Infrared Phys. Technol. 67 (2014), 477–489.
112. Wang, Z., Wang, S., Zhu, Y., et al. Review of image fusion based on pulse-coupled neural network. Arch. Comput. Methods Eng., 2015, 1–13.
113. Du, J., Li, W., Lu, K., et al. An overview of multi-modal medical image fusion. Neurocomputing 215 (2016), 3–20.
114. Bai, Luyi, Xu, Changming, Wang, Cong, A review of fusion methods of multi-spectral image. Optik 126 (2015), 4804–4807.
115. Ghassemian, Hassan, A review of remote sensing image fusion methods. Inform. Fusion 32 (2016), 75–89.
116. Yang, B., Jing, Z., Zhao, H., Review of pixel-level image fusion. J. Shanghai Jiaotong Univ. (Science) 15:1 (2010), 6–12, 10.1007/s12204-010-7186-y.
117. Ghiass, R.S., Arandjelović, O., Bendada, A., et al. Infrared face recognition: a comprehensive review of methodologies and databases. Pattern Recogn. 47:9 (2014), 2807–2824.
118. S. Wu, Z. Fang, Z. Xie, W. Liang, Recent Advances in Face Recognition-Chapter 13: Blood Perfusion Models for Infrared Face Recognition, InTech, 2008.
119. Yin, M., Duan, P., Liu, W., et al. A novel infrared and visible image fusion algorithm based on shift-invariant dual-tree complex shearlet transform and sparse representation. Neurocomputing 226 (2017), 182–192.
120. Yin, Haitao, Li, Shutao, Fang, Leyuan, Simultaneous image fusion and super-resolution using sparse representation. Inform. Fusion 14 (2013), 229–240.
121. Xiaoqi, Lu, Baohua, Zhang, Ying, Zhao, He, Liu, Haiquan, Pei, The infrared and visible image fusion algorithm based on target separation and sparse representation. Infrared Phys. Technol. 67 (2014), 397–407.
122. He, Kangjian, Zhou, Dongming, Zhang, Xuejie, Nie, Rencan, Wang, Quan, Jin, Xin, Infrared and visible image fusion based on target extraction in the nonsubsampled contourlet transform domain. J. Appl. Remote Sens., 11(1), 2017, 015011.
123. Chen, Yong, Xiong, Jie, Liu, Huan-lin, Fan, Qiang, Fusion method of infrared and visible images based on neighborhood characteristic and regionalization in NSCT domain. Optik 125 (2014), 4980–4984.
124. Adu, Jianhua, Gan, Jianhong, Wang, Yan, Huang, Jian, Image fusion based on nonsubsampled contourlet transform for infrared and visible light image. Infrared Phys. Technol. 61 (2013), 94–100.
125. Zhang, Qiong, Maldague, Xavier, An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing. Infrared Phys. Technol. 74 (2016), 11–20.
126. Tseng, Din-Chang, Liu, Yu-Shuo, Chou, Chang-Min, Image fusion with contrast improving and feature preserving. Math. Problems Eng. 2015:603195 (2015), 1–14.
127. Bai, Xiangzhi, Morphological center operator based infrared and visible image fusion through correlation coefficient. Infrared Phys. Technol. 76 (2016), 546–554.
128. Zhao, P., Image-fusion-based contour extraction scheme. Opt. Eng., 44(12), 2005 127005:1-127005:7.
129. Zhenfeng, Shao, Jun, Liu, Qimin, Cheng, Fusion of infrared and visible images based on focusmeasure operators in the curvelet domain. Appl. Opt. 51:12 (2012), 1910–1921.
130. Chen, Yanfei, Sang, Nong, Attention-based hierarchical fusion of visible and infrared images. Optik 126 (2015), 4243–4248.
131. Kun, Liu, Lei, Guo, Huihui, Li, Jingsong, Chen, Fusion of infrared and visible light images based on region segmentation. Chin. J. Aeronaut. 22 (2009), 75–80.
132. Li, Huafeng, Qiu, Hongmei, Yu, Zhengtao, Zhang, Yafei, Infrared and visible image fusion scheme based on NSCT and low-level visual features. Infrared Phys. Technol. 76 (2016), 174–184.
133. Fu, Zhizhong, Wang, Xue, Xu, Jin, Zhou, Ning, Zhao, Yufei, Infrared and visible images fusion based on RPCA and NSCT. Infrared Phys. Technol. 77 (2016), 114–123.
134. Madheswari, K., Venkateswaran, N., Swarm intelligence based optimization in thermal image fusion using dual tree discrete wavelet transform. Quantitat. InfraRed Thermogr. J. 14 (2017), 24–43.
135. Wang, Zhuozheng, Deller, J.R. Jr., Fleet, Blair D., Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis. J. Electron. Imaging, 25(1), 2016, 013007.
136. Li, S., Yang, B., Hu, J., Performance comparison of different multi-resolution transforms for image fusion. Inform. Fusion 12:2 (2011), 74–84.
137. Bavirisetti, Durga Prasad, Dhuli, Ravindra, Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform. IEEE Sens. J. 14:10 (2015), 203–209.
138. Cvejic, Nedeljko, Bull, David, Canagarajah, Nishan, Region-based multimodal image fusion using ICA bases. IEEE Sens. J. 7:5 (2007), 743–751.
139. Yin, Songfeng, Cao, Liangcai, Ling, Yongshun, Jin, Guofan, Fusion of noisy infrared and visible images based on anisotropic bivariate shrinkage. Infrared Phys. Technol. 54 (2011), 13–20.
140. Toet, A., Valeton, J.M., van Ruyven, L.J., Merging thermal and visual images by a contrast pyramid. Opt. Eng. 28:7 (1989), 789–792.
141. 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. Inform. Fusion 30 (2016), 15–26.
142. Zhao, Jufeng, Zhou, Qiang, Chen, Yueting, Feng, Huajun, Xu, Zhihai, Li, Qi, Fusion of visible and infrared images using saliency analysis and detail preserving based image decomposition. Infrared Phys. Technol. 56 (2013), 93–99.
143. Yan, X., Qin, H., Li, J., et al. Infrared and visible image fusion using multiscale directional nonlocal means filter. Appl. Opt. 54:13 (2015), 4299–4308.
144. Gan, Wei, Wu, Xiaohong, Wu, Wei, Yang, Xiaomin, Ren, Chao, He, Xiaohai, Liu, Kai, Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter. Infrar. Phys. Technol. 72 (2015), 37–51.
145. Ma, J., Zhou, Z., Wang, B., et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization. Infrar. Phys. Technol. 82 (2017), 8–17.
146. Bavirisetti, Durga Prasad, Dhuli, Ravindra, Two-scale image fusion of visible and infrared images using saliency detection. Infrared Phys. Technol. 76 (2016), 52–64.
147. Zhao, Jufeng, Gao, Xiumin, Chen, Yueting, Feng, Huajun, Wang, Daodang, Multi-window visual saliency extraction for fusion of visible and infrared images. Infrared Phys. Technol. 76 (2016), 295–302.
148. Zhao, J., Cui, G., Gong, X., et al. Fusion of visible and infrared images using global entropy and gradient constrained regularization. Infrared Phys. Technol. 81 (2017), 201–209.
149. Zhao, Peng, Xu, Ting-Fa, Ni, Guo-Qiang, Object's contour detection scheme based on image fusion. Int. J. Comput. Math. 87:13 (2010), 2857–2870.
150. Zhou, Xin, Yin, Xin, Liu, Rui-An, Wang, Wei, Infrared and visible image fusion technology based on directionlets transform. EURASIP J. Wireless Commun. Network., 2013, 42 2013.
151. Zhu, P., Huang, Z., Lei, H., Fusion of infrared and visible images based on BEMD and NSDFB. Infrared Phys. Technol. 77 (2016), 82–93.
152. Yang, Fengbao, Wei, Hong, Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules. Infrared Phys. Technol. 60 (2013), 235–243.
153. Ma, Yong, Chen, Jun, Chen, Chen, Fan, Fan, Ma, Jiayi, Infrared and visible image fusion using total variation model. Neurocomputing 202 (2016), 12–19.
154. Ma, Jiayi, Chen, Chen, Li, Chang, Huan, Jun, Infrared and visible image fusion via gradient transfer and total variation minimization. Inform. Fusion 31 (2016), 100–109.
155. Haghighat, Mohammad Bagher Akbari, Aghagolzadeh, Ali, Seyedarabi, Hadi, A non-reference image fusion metric based on mutual information of image features. Comput. Electr. Eng. 37 (2011), 744–756.
156. Li, H., Zhang, J., Sun, J., A visual analytics approach for deterioration risk analysis of ancient frescoes. J. Visualizat., 1–14, 2016.
157. Toet, A., Ijspeert, J.K., Waxman, A.M., et al. Fusion of visible and thermal imagery improves situational awareness. Displays 18:2 (1997), 85–95.
158. Liu, Yu, Liu, Shuping, Wang, Zengfu, A general framework for image fusion based on multi-scale transform and sparse representation. Inform. Fusion 24 (2015), 147–164.
159. Wang, Z., Bovik, A.C., A universal image quality index. IEEE Signal Process. Lett. 9:3 (2002), 81–84.
160. G. Piella, H. Heijmans, A new quality metric for image fusion, in: Proceedings of the Tenth International Conference on Image Processing, Barcelona, Spain, 2003, pp. 173–176.
161. Xydeas, C.S., Petrovic, V.S., Objective image fusion performance measure. Electron. Lett. 36:4 (2000), 308–309.
162. Qu, G., Zhang, D., Yan, P., Information measure for performance of image fusion. Electron. Lett. 38:7 (2002), 313–315.
163. H.R. Sheikh, A.C. Bovik, Image information and visual quality, in: Proceedings-ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 3, 2004, iii-709-12.
164. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13 (2004), 600–612.
165. http://www.ece.uwaterloo.ca/~z70wang/research/ssim.
166. Zhao, J., Laganiere, R., Liu, Z., Performance assessment of combinative pixel-level image fusion based on an absolute feature measurement. Int. J. Innovative Comput. Inf. Control 6:A3 (2007), 1433–1447.
167. Sheikh, H.R., Bovik, A.C., Image information and visual quality. IEEE Trans. Image Process. 15:2 (2006), 430–444.
168. Hong, Li, Fang, Liu, Shuyuan, Yang, Kai, Zhang, Remote sensing image fusion based on deep support value learning networks. Chin. J. Comput. 39:8 (2016), 1583–1596.
169. Zhong, J., Yang, B., Huang, G., et al. Remote sensing image fusion with convolutional neural network. Sens. Imag. 17:1 (2016), 1–16.
170. Liu, Y., Chen, X., Peng, H., et al. Multi-focus image fusion with a deep convolutional neural network. Inform. Fusion 36 (2016), 191–207.
171. Hou, W., Gao, X., Tao, D., et al. Blind image quality assessment via deep learning. IEEE Transact. Neural Networks Learn. Syst. 26:6 (2015), 1275–1286.
172. S. Bianco, L. Celona, P. Napoletano, et al. On the Use of Deep Learning for Blind Image Quality Assessment, 2016. https://arxiv.org/abs/1602.05531.
173. Hou, W., Gao, X., Tao, D., et al. Blind image quality assessment via deep learning. IEEE Transact. Neural Networks Learn. Syst. 26:6 (2017), 1275–1286.
174. Kang, L., Ye, P., Li, Y., et al. Convolutional neural networks for no-reference image quality assessment. CVPR IEEE, 2014, 1733–1740.
175. Gao, Shaoshu, Jin, Weiqi, Wang, Lingxue, Quality assessment for visible and infrared color fusion images of typical scenes. Chin. Opt. Lett. 8 (2012), 18–22.
176. Yu, X., Chen, Q., Gu, G., et al. Perceived assessment metrics for visible and infrared color fused image quality without reference image. Opt. Rev. 22:1 (2015), 109–122.
177. Ledda, P., Chalmers, A., Troscianko, T., et al. Evaluation of tone mapping operators using a High Dynamic Range display. ACM Transact. Graph. 24:3 (2005), 640–648.