[1] 杨风暴. 红外物理与技术 [M].北京: 电子工业出版社, 2014. YANG Fengbao. Infrared Physics and Technology[M]. Beijing: Publishing House of Electronics Industry, 2014.

[2] 张彬, 杨风暴. 小波分析方法及其应用[M].北京: 国防工业出版社, 2011. ZHANG Bin, YANG Fengbao. Wavelet Analysis Method and Its Application[M]. Beijing: National Defense Industry Press, 2011.

[3] Amolins K, Zhang Y, Dare P. Wavelet based image fusion techniques — An introduction, review and comparison[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2007, 62(4): 249-263.

[4] 黄奎. 基于小波变换的图像处理综述 [J]. 价值工程 , 2015, 34(8): 255-256. HUANG Kui. Overview of Image Processing Based on Wavelet Transform[J]. Value Engineering, 2015, 34(8): 255-256.

[5] 龚建周, 刘彦随, 夏北成, 等. 小波基及其参数对遥感影像融合图像质量的影响[J].地理与地理信息科学 , 2010, 26(2): 6-10. GONG Jianzhou, LIU Yansui, XIA Beicheng, et al. Effect of Wavelet Basis and Decomposition Levels on Performance of Fusion Images from Remotely Sensed Data[J]. Geography and Geo-Information Science, 2010, 26(2): 6-10.

[6] 安富, 杨风暴 , 蔺素珍 , 等. 特征差异驱动的红外偏振与光强图像融合[J]. 中国科技论文, 2014(1): 96-102. AN Fu, YANG Fengbao, LIN Suzhen, et al. Feature differences driven fusion of infrared polarization and intensity images[J]. China Sciencepaper, 2014(1): 96-102.

[7] 牛涛, 杨风暴 , 王肖霞 , 等. 差异特征与融合算法的集值映射关系的建立[J].红外与激光工程 , 2015, 44(3): 1073-1079. NIU Tao, YANG Fengbao, WANG Xiaoxia, et al. Establishment of set-valued mapping between difference characteristics and fusion algorithms[J]. Infrared and Laser Engineering, 2015, 44(3): 1073-1079.

[8] 方丽萍, 张馨方, 罗启姣, 等. 管道泄漏信号去噪中小波基及分解尺度的选取——基于小波分析[J].钦州学院学报 , 2017, 32(5): 18-22. FANG Liping, ZHANG Xinfang, LUO Qijiao, et al. Wavelet Basis and Decomposition Scale Selection in Pipeline Leakage Signal Denoising——Based on wavelt[J]. Journal of Qinzhou University, 2017, 32(5): 18-22.

[9] Sappa A D, Carvajal J A, Aguilera C A, et al. Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study[J]. Sensors, 2016, 16(6): 861-876.

[10] 杨艳春, 李娇, 王阳萍 . 图像融合质量评价方法研究综述 [J].计算机科学与探索, 2018, 12(7): 6-20. YANG Yanchun, LI Jiao, WANG Yangping. Review of Image Fusion Quality Evaluation Methods[J]. Journal of Frontiers of Computer Science and Technology, 2018, 12(7): 6-20.

[11] 王跃华, 陶忠祥. 红外与可见光图像融合质量评价方法综述 [J].红外, 2012, 33(6): 7-11. WANG Yuehua, TAO Zhongxiang. Overview of Quality Evaluation Methods of Fused Infrared and Visible Images[J]. Infrared, 2012, 33(6): 7-11.

[12] LIU Z, Blasch E, XUE Z, et al. Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 34(1): 94-109.

[13] Ayd, Taner Z O, Alpayd, et al. Cost-conscious comparison of supervised learning algorithms over multiple data sets[M]. Elsevier Science Inc. 2012.

[14] Trawiński B, Sm.tek M, Telec Z, et al. Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms[J]. International Journal of Applied Mathematics & Computer Science, 2012, 22(4): 449-471.

[15] Derrac J, García S, Molina D, et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J]. Swarm & Evolutionary Computation, 2011, 1(1): 3-18.

[16] Garc, Salvador A, Ndez A, et al. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power[J]. Information Sciences, 2010, 180(10): 2044-2064.

[17] Noble W S. How does multiple testing correction work [J]. Nature Biotechnology, 2009, 27(12): 1135-1137.

[18] Bergmann B, Hommel P G. Improvements of General Multiple Test Procedures for Redundant Systems of Hypotheses[M]//Multiple Hypothesenprüfung / Multiple Hypotheses Testing. Springer Berlin Heidelberg, 1988: 3-7.