一种基于小波变换的红外偏振融合算法

针对红外偏振图像可以较好地抑制背景噪声，对目标边缘信息比较敏感的特点，提出一种基于小波变换的红外偏振融合算法，它主要用于红外辐射强度图像和偏振度图像融合，增加图像的信息量。首先采用小波变换对参与融合的每幅图像分别进行各尺度分解，得到各尺度小波系数，然后针对不同尺度小波系数，采用邻域平均梯度为判据进行融合，得到融合后的各尺度小波系数，最后通过小波逆变换进行图像重构，得到融合图像。融合前后的图像对比表明融合图像在保留辐射强度图像的清晰度的同时，突出了目标的边缘、轮廓信息。相对于辐射强度图像，融合图像的梯度均值提高了112%，相对于偏振度图像，融合图像的标准差提高了151%，信息熵提高了38%。

Polarization image fusion algorithm based on wavelet transform

Based on the characteristics that the infrared polarization image can restrain background noise greatly, and can be more sensitive to target edge information, a polarization image fusion algorithm based on wavelet transform is proposed. It is mainly used in image fusion between the infrared radiation intensity image and the polarization degree image in order to increase the amount of information of the image. First, wavelet transform can make different wavelet scaling decomposition in each involved image respectively, and get the wavelet coefficients of each scale. Second, it uses the method of neighborhood average gradient on each scaling wavelet coefficients to get each scaling wavelet fusion coefficients. Last, it makes image reconstruction based on wavelet transform to get fused image. The comparison between the images and fused images shows that this method can keep image clarity of the radiation intensity image, as well as highlight the edge and contour information. Compared to the radiation intensity image, the average gradient of fused images increases by 112%, while compared to the polarization degree image, the standard deviation of fused images increases by 151% ,and the information entropy of fused images increases by 38%.