Firstly a method of bilateral filter and dynamic range partitioning (BF & DRP) was used to improve the details of the low SNR and low contrast original infrared image, by which the edges of targets were strengthened, the noises were suppressed, and the constrast of infrared image was enhanced. Secondly, and finally, the multi-scale retinex transform was used to improve the fusion of visible and infrared image, by combining the multi-scale transform and regional fusion where the adaptive low frequency and high frequency coefficient were considered, which effectively suppressed the noises and enhanced the details..
Experimental results proved the effectiveness of the proposed image fusion method. The salient color and texture feature of visible image was well preserved, the important details of infrared and visible image were highlighted. The results show that this algorithm is better than traditional image fusion method, such as wavelet transform, non-sampled contourlet transform, in in standard deviation, information entropy and Average gradient etc.. the algorithm of this paper is able to preserve the details of image, increase the amount of importance characteristic information, is advantageous to the visual performance and distinguishability of fused image for human observation.