BEMD image fusion based on PCNN and compressed sensing

Abstract

Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method, which combines pulse coupled neural network (PCNN) and compressed sensing, and uses them in the BEMD. At first, BEMD processes the original images decomposed into multiple bidimensional intrinsic mode function (BIMFs) and a residual image. Then after doing compression measurement on each layer of BIMFS, we can get compression measurement coefficients. The coefficients at the same layer do the PCNN image fusion, and we can get measurement sampling BIMFs. And then after measurement sampling BIMFs reconstructed, we can get the final BIMFs. The residual images do the fusion based on entropy weight to get the final residual image. At last, after BEMD inverse transform, the final BIMFs and the final residual image get the result image. Experimental studies have shown that compared with other multi-scale decompositions-based image fusion algorithms, the algorithm in this paper has a better performance in terms of objective criteria and visual appearance.