Image fusion algorithm based on contourlet domain hidden Markov tree models

A novel image fusion method based on Contourlet domain hidden Markov tree models is proposed. Contourlet transform provides a flexible multiresolution, local and directional image expansion, and also a sparse representation for two-dimensional piecewise smooth signals building images. Contourlet HMT can capture all inter-scale, inter-direction, and inter-location dependencies of the Contourlet coefficients. Aiming at the different frequency bands of Contourlet decomposition with different characteristics, different fusion rules are applied to different subbands. In the low-frequency information, the weighted average mean is used to obtain the fused low-frequency information. Contourlet HMT is applied to design low-frequency information rule, the fusion method has the ability to strengthen the relationship among the Contourlet coefficients, extract more detailed and exact information from the original images. The fused images by the proposed algorithm exhibit good performance both in subjective and objective standards. Experimental results also show the simplicity and effectiveness of the method and its advantages over the conventional approaches.