Abstract：Aiming the scene contrast is low in the fusion process of the mid-wave infrared image with a detection band of 3.7 to 4.8 μm and the long-wave infrared image with a detection band of 8 to 14 μm, the saliency target is not enough to protrude, and the artifacts introduce serious problems. In this paper, Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) is used to the multi-scale decomposition of infrared medium-wave and long-wave images to obtain two-dimensional intrinsic mode functions (BIMFs) and residual components (Residual). For each layer of bidimensional intrinsic mode function, this paper’s improved local energy window fusion rule is selected. First, a weighting operator is configured to increase the central pixel’s energy proportion for the regional window. In this paper, different weighting operators are selected, which have been verified to effectively highlight the energy characteristic information of medium-wave and long-wave images by experiments, and secondly, the phase information of BIMFs is fully used, when the phases are opposite, the energy weighted average method is used to solve the problem that the polarity sign of the fusion coefficient is difficult to determine. When the phases are the same, the energy gap is judged, and the set fusion rule is selected according to the size of the gap based on the grayscale difference characteristics of the infrared medium wave and long wave images. Using the infrared mid-wave image and improved the regional energy window of Saliency detection of maximum symmetric surround weight map guides the fusion of base layer coefficients for the residual components. The adaptive local surround window makes full use of the low-frequency saliency information and has a very good suppression effect on the useless background. It can highlight the saliency objects in the complex background image, and finally obtain the guidance image with rich details and obvious contrast. Finally, the fusion image is obtained through the inverse reconstruction process of FABEMD, and subjective and objective performance evaluations are performed on five sets of infrared medium and long-wave images with different backgrounds and different sizes. The four sets of images are all taken from multi-band infrared acquisition systems and are strictly registration and comparative experiments with 7 related algorithms. In terms of subjective performance, salient objects is standing out and the clarity is high, the objective performance is excellent in two evaluation indicators of average gradient and spatial frequency, the effectiveness of this algorithm is verified.
Key words：Images fusion; Fast and adaptive bidimensional empirical mode decomposition; Phase information; Regional energy window; Saliency map
崔晓荣，沈 涛，黄建鲁，孙宾宾. FABEMD和改进局部能量窗口的红外中波和长波图像融合[J]. 光谱学与光谱分析, 2021, 41(07): 2043-2049.
CUI Xiao-rong, SHEN Tao, HUANG Jian-lu, SUN Bin-bin. Infrared Mid-Wave and Long-Wave Image Fusion Based on FABEMD and Improved Local Energy Window. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2043-2049.
[1] Rogalski A. Proc. SPIE, 2017, 10433: 1.
[2] LIU Feng, SHEN Tong-sheng, GUO Shao-jun, et al(刘 峰, 沈同圣, 郭少军, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(6): 1934.
[3] QIU Rong-chao, LOU Shu-li, LI Ting-jun, et al(仇荣超, 娄树理, 李廷军, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(3): 698.
[4] Bhuiyans M A, Adhami R R, Khan J F. Eurasip Journal on Advances in Signal Processing, 2008, 2008(1): 1.
[5] LIN Zi-hui，WEI Yu-xing，ZHANG Jian-lin，et al(林子慧, 魏宇星, 张建林, 等). Infrared Technology(红外技术), 2019, 41(7): 640.
[6] Cheng M M, Mitra N J, Huang X, et al. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2015, 37(3): 569.
[7] FU Zhi-zhong, WANG Xue, LI Xiao-feng, et al(傅志中, 王 雪, 李晓峰, 等). Journal of University of Electronic Science and Technology of China(电子科技大学学报), 2017，46(2): 357.
[8] Achanta R, Susstrunk S. Saliency Detection Using Maximum Symmetric Surround. IEEE International Conference on Image Processing, 2010: 2653, doi: 10.1109/ICIP.2010.5652636.
[9] AN Ying, FAN Xun-li, CHEN Li, et al(安 影, 范训礼, 陈 莉, 等). Systems Engineering and Electronics(系统工程与电子技术), 2019(11): CN 11-2422/TN.
[10] PENG Zhi-yong, WANG Xiang-jun, LU Jin(彭志勇, 王向军, 卢 进). Infrared and Laser Engineering(红外与激光工程), 2014, 43(6): 1772.
[11] Yang Jinku, Guo Lei, Yang Haiku. IEEJ Transactions on Electrical and Electronic Engineering, IEEJ Trans，2015, 10: 447.
[12] Yin Ming, Liu Xiaoning, Liu Yu, et al. IEEE Transactions on Instrumentation and Measurement, 2018，(99)：1.
[13] Cui G, Feng H, Xu Z, et al. Optics Communications, 2015, (341): 199.
