Image fusion base on improved contourlet transform

The purpose of image fusion is to obtain an iamge from multiple images, this image should be able to reflect the important information of all original images. Contourlet transform, not only has characteristics of multiresolution locality and critical sampling which wavelet has but also has the characteristics of multiple decomposition directions and anisotropy which wavelets lacking. Energy is a statistical parameter of describe the texture feature. So we apply the Max Energy and Contourlet transform combined for image fusion. Entropy expreses the average amount of information. The distribution of standard deviation reflects the degree of dispersion of the image.The average gradient reflects the clarity of the image, the contrast of small details and the feature of texture transform. Contrast with wavelet transform, laplace transform, weighted transform, the traditional of contourlet transform, on evaluation by Entropy, standard deviation and average gradient, experimental results from this algorithms for fusion with infrared image and visual image were better than other algorithms.