Abstract
Aiming at the different characteristics of the infrared image and visible light image, the paper proposed a kind of fusion algorithm for the infrared image and visible light image based on non-subsampled contourlet transform. Firstly, the source images are made multiscale and multi-direction decomposition by using non-subsampled contourlet transform (NSCT). Secondly, to decomposed low frequency subband, a decision-making value with regional energy and variance is constructed and used in fusing the coefficients by choosing larger decision-making value. And for the decomposed high frequency subband, different fusion rules are employed for different levels. The fusion rule of selecting large absolute value of pixel is used for the highest level, and the fusion rule of selecting large regional variance based on regional energy matching degree is used to fuse the other levels. Finally, the final fused image is reconstructed by using the non-subsampled contourlet inverse transform. The experimental results have shown that the proposed algorithm can get more detail information and can exhibit better fusion performance. Key words: Fusion algorithm, regional energy matching degree, image fusion, non-subsampled contourlet transform, infrared image, visible light image, shift invariant.
Highlights
Image fusion refers to the information process of integrating the images or image sequence information of the particular scene which two or more than two sensors get at the same time or at the different time to generate a new interpretation about this scene (Lian et al, 2011).The fusion of the infrared image and visible light image is an image fusion method which has been widely used in military and security monitoring field
The visible light image is abundant in the spectral information, large in the dynamic range, relatively high in the contrast ratio and abundant in the spectral information (Ye et al, 2008)
Because what the low layer reflects is the coarse information, so the fusion method of selecting large regional variance based on the regional energy matching degree is used to maintain the relationship between the pixel neighborhoods better so as to make the edge lines more natural
Summary
Image fusion refers to the information process of integrating the images or image sequence information of the particular scene which two or more than two sensors get at the same time or at the different time to generate a new interpretation about this scene (Lian et al, 2011). The fusion method of the harmony of the energy variance decision selection based on regional energy matching degree and the weighted average is used to select the fusion coefficients of the low frequency subband. Because what the low layer reflects is the coarse information, so the fusion method of selecting large regional variance based on the regional energy matching degree is used to maintain the relationship between the pixel neighborhoods better so as to make the edge lines more natural. (1) The high frequency coefficient on the highest layer whose decomposition scale is J (here take J=4) of the fusion image is as shown in Equation (8). (2) The high frequency coefficient on the (J-l) (l≥1) layer whose decomposition scale is (J-l) of the fusion image is fused by the method of selecting larger region variance based on the regional energy matching degree. The fusion rules are as follows: if MI ,V x , y , high frequency coefficient is as shown in Equation (9)
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