[1] [1] TOET A, FRANKEN E M. Perceptual evaluation of different image fusion schemes［J］. Displays, 2003, 24(1): 25-37.

[2] [2] PETROVIC′ V. Subjective tests for image fusion evaluation and objective metric validation［J］. Information Fusion, 2007, 8(2): 208-216.

[3] [3] CUI Yan-mei, NI Guo-qiang, ZHONG Yan-li, et al. Analysis and evaluation of the effect of image fusion using statistics parameters［J］. Journal of Beijing Institute of Technology, 2000, 20(1): 102-106.

[4] [4] QU Gui-hong, ZHANG Da-li, YAN Ping-fan. Information measure for performance of image fusion［J］. Electronics Letters, 2002, 38(7): 313-315.

[5] [5] HOWELL C, MOORE R, BURKS S, et al. An evaluation of fusion algorithms using image fusion metrics and human identification performance［C］. SPIE, 2007: 65430V1-65430V11.

[6] [6] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity［J］. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

[7] [7] ZHANG Jing, SHEN Lan-sun, GAO Jing-jing. Regions of interest detection based on visual attention mechanism［J］. Acta Photonica Sinica, 2009, 38(6): 1561-1565.

[8] [8] XING Su-xia, CHEN Tian-hua. A new quality measure method for image fusion based on visual characteristics［J］. Journal of Beijing Technology and Business University, 2010, 28(1): 60-63.

[9] [9] DI Hong-wei, LIU Xian-feng. Image fusion quality assessment based on structural similarity［J］. Acta Photonica Sinica, 2006, 35(5): 766-771.

[10] [10] PIELLA G. A general framswork for multiresolution image fusion: from pixels to regions［J］. Information Fusion, 2003, 4(4): 259-280.