Fusion of polarimetric image using contourlet transform

Abstract

Contourlet transform is the combination of the multi-scale analysis and multi-directional analysis in processing high-dimensional signals and has better approximation precision and better sparse description. Firstly, Using the Contourlet transform, several polarimetric images can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of region variance information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused contourlet coefficients. Experimental results show that the proposed algorithm works better in preserving the edges and texture information compared with the traditional image fusion algorithms.