Regional feature self-adaptive image fusion algorithm based on contourlet transform

Contourlet transform overcomes the weakness of wavelet transform in dealing with high-dimensional signals, it provides a flexible multiresolution, local and directional image expansion and a sparse representation for two-dimensional piecewise smooth signal resembling images. It can satisfy the anisotropy scaling relation for curves, and thus offers a fast and structured curvelet-like decomposition. When contourlet transform is applied to image fusion, the characteristic of original images can be effectively extracted and more important information is preserved. The fusion algorithm based on contourlet transform can be divided into three steps. Firstly, the original images are decomposed with contourlet transform. Secondly, because different fusion rules fit different frequency bands, we designed the regional feature self-adaptive fusion rule is used in high-frequency domain. Finally the fused coefficients are reconstructed to obtain fusion results. Two sets images are taken as experimental data, subjective and objective standards are used to evaluate the results, and comparison with results based on wavelet transform is also carried out. The results show that this method gets better fusion results than wavelet transform. And the regional feature self-adaptive image fusion algorithm based on contourlet transform is an effective and feasible algorithm.