Region-based statistical signal processing scheme for image fusion

A new image fusion scheme based on region statistical signal processing was proposed. The region growing technique using gray-level clustering was employed to segment the source images into different regions whose borderline represented with crack edge. The registered source images and their segmented mapping were decomposed into a multi-resolution representation with both low-frequency coarse information and high-frequency detail information respectively. The expectation maximization algorithm modeled with noise statistic distribution was used to fuse the low-frequency coarse information of the registered images, while the match and salience measures were applied to fuse the high-frequency detail information of the registered images. The final fused image was obtained by taking the inverse transform of the composite multi-resolution representations information. Fusion experiments on real world images indicate that the proposed method is effective and efficient, which achieves better performance than the most generic fusion method.