An Image Fusion Method Based on Region Segmentation and Cauchy Convolution

A novel image fusion algorithm performed on the feature level is proposed incorporating with region segmentation and Cauchy convolution. Firstly, the fuzzy c-means clustering algorithm(FCM) is used to segment the image in the space of feature difference, which is formed by dual-tree discrete wavelet transform(DT-DWT) sub-bands. Secondly, the high frequency coefficients are modeled by the convolution of Cauchy distributions and the weights are optimized via Maximum Likelihood(ML) estimation. Finally, the fused image is obtained by taking the inverse DT-DWT. The image fusion method solved the uncertain problem of two-region segmentation in the space of feature difference, and the model of Cauchy convolution leads to a more accurate and reliable optimization process. Experiments show that the proposed method is effective and has good visual perception.