Reflectance estimation for infrared and visible image fusion |
Gu, Yan
(North Night Vision Technology Corp., Ltd.)
Yang, Feng (North Night Vision Technology Corp., Ltd.) Zhao, Weijun (North Night Vision Technology Corp., Ltd.) Guo, Yiliang (North Night Vision Technology Corp., Ltd.) Min, Chaobo (The College of Internet of Things Engineering, HoHai University) |
1 | Y. Liu, S. Liu and Z.Wang, "A general framework for image fusion based on multi-scale transform and sparse representation," Inf. Fusion, vol. 24, pp. 147-164, Jul. 2015. DOI |
2 | Y. Liu, X. Chen, H. Peng, Z. Wang, "Multi-focus image fusion with a deep convolutional neural network," Inf. Fusion, vol. 36, pp. 191-207, Jul. 2017. DOI |
3 | J. Ma, C. Chen, C. Li and J. Huang, "Infrared and visible image fusion via gradient transfer and total variation minimization," Inf. Fusion, vol. 31, pp. 100-109, Sep. 2016. DOI |
4 | J. Qu, Y. Li, Q. Du and H. Xia, "Hyperspectral and panchromatic image fusion via adaptive tensor and multi-scale retinex algorithm," IEEE Access, vol. 8, pp. 30522-30532, Feb. 2020. DOI |
5 | D. J. Jobson, Z. Rahman, and G. A. Woodell, "A multiscale Retinex for bridging the gap between color images and the human observation of scenes," IEEE Trans. Image Process., vol. 6, no. 7, pp. 965-976, Jul. 1997. DOI |
6 | Y. Liu, X. Chen, R. K. Ward and Z. J. Wang, "Image fusion with convolutional sparse representation," IEEE Signal Process. Lett., vol. 23, no. 12, pp. 1882-1886, Dec. 2016. DOI |
7 | J. Wang, J. Peng, X. Feng, G. He and J. Fan, "Fusion method for infrared and visible images by using non-negative sparse representation," Infrared Phys. Technol., vol. 67, pp. 477-489, Nov. 2014. DOI |
8 | W. Kong, Y. Lei and H. Zhao, "Adaptive fusion method of visible light and infrared images based on non-subsampled shearlet transform and fast non-negative matrix factorization," Infrared Phys. Technol., vol. 67, pp. 161-172, Nov. 2014. DOI |
9 | P. Hill, M. E. Al-Mualla and D. Bull, "Perceptual image fusion using wavelets," IEEE Trans. Image Process., vol. 26, no. 3, pp. 1076-1088, Mar. 2017. DOI |
10 | M. Choi, R. Kim, M. Nam and H. Kim, "Fusion of multispectral and panchromatic satellite images using the curvelet transform," IEEE Geosci.Remote Sens. Lett., vol. 2, no. 2, pp. 136-140, Apr. 2005. DOI |
11 | T. Xiang, L. Yan and R. Gao, "A fusion algorithm for infrared and visible images based on adaptive dual-channel unit-linking PCNN in NSCT domain," Infrared Phys. Technol., vol. 69, pp. 53-61, Mar. 2015. DOI |
12 | N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Syst. Man Cy., vol. 9, no. 1, pp. 62-66, Jan. 1979. DOI |
13 | F. Meng, M. Song, B. Guo, R. Shi and D. Shan, "Image fusion based on object region detection and non-subsampled contourlet transform," Comput. Electr. Eng., vol. 62, pp. 375-383, Aug. 2017. DOI |
14 | X. Zhang, Y. Ma, F. Fan, Y. Zhang and J. Huang, "Infrared and visible image fusion via saliency analysis and local edge-preserving multi-scale decomposition," JOSA A., vol. 34, no. 8, pp. 1400-1410, Aug. 2017. DOI |
15 | J. Zhao, Y. Chen, H. Feng, Z. Xu and Q. Li, "Infrared image enhancement through saliency feature analysis based on multi-scale decomposition," Infrared Phys. Technol., vol. 62, pp. 86-93, Jan. 2014. DOI |
16 | J. W. Roberts, J. Van Aardt and F. Ahmed, "Assessment of image fusion procedures using entropy, image quality, and multispectral classification," J. Appl. Remote Sens., vol. 2, no. 1, pp. 023522, Jan. 2008. DOI |
17 | A. M. Eskicioglu and P. S. Fisher, "Image quality measures and their performance," IEEE Trans. Commun., vol. 43, no. 12, pp. 2959-2965, Dec. 1995. DOI |
18 | A. Toet, "Image fusion by a ratio of low-pass pyramid," Pattern Recognit. Lett., vol. 9, no. 4, pp. 245-253, May. 1989. DOI |
19 | H. Li, X.-J. Wu, J. Kittler, "Infrared and visible image fusion using a deep learning framework," in Proc. of International Conference on Pattern Recognition, pp. 2705-2710, Nov. 2018. |
20 | X. Mao, Q. Li, H. Xie, R.Y. Lau, Z. Wang, S.P. Smolley, "Least squares generative adversarial networks," in Proc. of IEEE International Conference on Computer Vision, pp. 2813-2821, Dec. 2017. |
21 | E. H. Land, "The Retinex theory of color vision," Sci. Amer., vol. 237, no. 6, pp. 108-128, Dec. 1977. DOI |
22 | X. Fu, D. Zeng, Y. Huang, X. Zhang, and X. Ding, "A weighted variational model for simultaneous reflectance and illumination estimation," in Proc. of IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2782-2790, Jun. 2016. |
23 | D. J. Jobson, Z. Rahman, and G. A. Woodell, "Properties and performance of a center/surround Retinex," IEEE Trans. Image Process., vol. 6, no. 3, pp. 451-462, Mar. 1997. DOI |
24 | X. Ren, W. Yang, W. Cheng and J. Liu, "LR3M: robust low-light enhancement via low-rank regularized Retinex model," IEEE Trans. Image Process., vol. 29, pp. 5862-5876, Apr. 2020. DOI |
25 | D. P. Bavirisetti and R. Dhuli, "Two-scale image fusion of visible and infrared images using saliency detection," Infrared Phys. Technol., vol. 76, pp. 52-64, May. 2016. DOI |
26 | R. Yu, W. Chen and D. Zhou, "Infrared and visible image fusion based on gradient transfer optimization model," IEEE Access, vol. 8, pp. 50091-50106, Mar. 2020. DOI |
27 | M. Deshmukh and U. Bhosale, "Image fusion and image quality assessment of fused images," Int. J. Image Process., vol. 4, no. 5, pp. 484-508, Dec. 2010. |
28 | J. Ma, P. Liang, W. Yu, C. Chen, X. Guo, J. Wu and J. Jiang, "Infrared and visible image fusion via detail preserving adversarial learning," Inf. Fusion, vol. 54, pp. 85-98, Jul. 2019. DOI |
29 | C. Deng, Z. Wang, X. Li, H. Li and C. C. Cavalcante, "An improved remote sensing image fusion algorithm based on IHS transformation," KSII Trans. Int. Inf. Syst., vol. 11, no. 3, pp. 1633-1649, Mar. 2017. DOI |
30 | D. P. Bavirisetti, G. Xiao and G. Liu, "Multi-sensor image fusion based on fourth order partial differential equations," in Proc. of Int. Conf. on Inf. Fusion, pp. 701-709, July 10-13, 2017. |
31 | Z. Zhou, B.Wang, S. Li and M. Dong, "Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters," Inf. Fusion, vol. 30, pp. 15-26, Jul. 2016. DOI |
32 | W. Zhao, H. Lu and D. Wang, "Multisensor image fusion and enhancement in spectral total variation domain," IEEE Trans. Multimedia, vol. 20, no. 4, pp. 866-879, Apr. 2018. DOI |
33 | Y. Rao, "In-fibre bragg grating sensors," Meas. Sci. Technol., vol. 8, no. 4, pp. 355, 1997. DOI |
34 | Y. Han, Y. Cai, Y. Cao and X. Xu, "A new image fusion performance metric based on visual information fidelity," Inf. fusion, vol. 14, no. 2, pp. 127-135, Apr. 2013. DOI |
35 | Z. Wang and A. C. Bovik, "A universal image quality index," IEEE Signal Process. Lett., vol. 9, no 3, pp. 81-84, Mar. 2002. DOI |
36 | S. Li, X. Kang, J. Hu, "Image fusion with guided filtering," IEEE Trans. Image Process., vol. 22, no. 7, pp. 2864-2875, Jul. 2013. DOI |
37 | M. Yin, P. Duan,W. Liu and X. Liang, "A novel infrared and visible image fusion algorithm based on shift-invariant dual-tree complex shearlet transform and sparse representation," Neurocomputing, vol. 226, pp. 182-191, Feb. 2017. DOI |
38 | W. Kong, L. Zhang and Y. Lei, "Novel fusion method for visible light and infrared images based on NSST-SF-PCNN," Infrared Phys. Technol., vol. 65, pp. 103-112, Jul. 2014. DOI |
39 | M. H. Loke and R. D. Barker, "Rapid least-squares inversion of apparent resistivity pseudosections by a quasi-Newton method," Geophysical Prospecting, vol. 44, no. 1, pp. 131-152, Jan. 1996. DOI |
40 | J. Ma, W. Yu, P. Liang, C. Li and J. Jiang, "FusionGAN: a generative adversarial network for infrared and visible image fusion," Inf. Fusion, vol. 48, pp. 11-26, Aug. 2019. DOI |
41 | F. An, X. Zhou and D. Lin, "Multiscale self-coordination of bidimensional empirical mode decomposition in image fusion," KSII Trans. Int. Inf. Syst., vol. 9, no. 4, pp. 1441-1456, Apr. 2015. DOI |
42 | W. Jeong, B. Han, H. Yang and Y. Moon, "Real-time visible-infrared image fusion using multi-guided filter," KSII Trans. Int. Inf. Syst., vol. 13, no. 6, pp. 3092-3107, Jun. 2019. DOI |
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