Image fusion by multiple features in the propagated filtering domain

Abstract

Visual high-contrast information, such as texture and color, contained in input biomedical imaging data should be preserved as much as possible in the fused image. To preserve the high-intensity textural and color information from input images, an image fusion method is proposed in this paper that utilizes propagated filtering and multiple features from the input images as two modalities. The method includes three steps. First, the inputs are decomposed into multiscale coarse images containing edge information and multiscale detail images containing textural information obtained by propagated filtering using different window sizes. Second, an entropy-based rule is used to combine the coarse images to contain much more edge information. A multiple features-based rule, including luminance, orientation and phase, is used to combine the detail images with the aim of preserving textural information and color information with less distortion. Finally, the fused image is obtained by adding the fused coarse and fused detail images in spatial-domain transformation. The experimental results on the fusion of co-registered biomedical image show that the proposed method preserves textural information with high-intensity and true color information.