An algorithm for multi-sensor image fusion using maximum a posteriori and nonsubsampled contourlet transform

Multi-sensor image fusion draws an inference based on the information obtained from different sensors. Recently, wavelet and contourlet transforms have been widely used in multi-sensor image fusion. But these transforms have been inadequate in the representation of images due to their subsampling. Hence, a fusion algorithm based on Synthetic Aperture Radar (SAR) and Panchromatic (PAN) images in Nonsubsampled Contourlet Transform (NSCT) domain is proposed. NSCT gives flexible multiscale, multidirectional expansion for images. A high fusion accuracy is achieved by 'Maximum A Posteriori (MAP)' estimation based on Rayleigh and Laplacian probabilities for despeckling of SAR higher frequency coefficients. Subsequently, the despeckled SAR coefficient is directly fused with PAN coefficients using the newly developed Edge-based fusion rule. The combination of NSCT, MAP and Edge-fusion rule facilitates maximum preservation of the edge and the texture information. The performance of the proposed fusion algorithm is evaluated using reference and non-reference quality metrics. The results prove that the proposed method outperforms the existing NSCT methods by preserving maximum features.