Abstract: The traditional image fusion based on compressed sensing algorithm is to deal with the whole coefficients sparse,and low-frequency coefficients of wavelet decomposition is not sparse,resulting in the quality reduction of compression reconstruction.Besides,the traditional fusion rules are difficult and not comprehensive to extrat characteristic value of high frequency coefficient.To solve this problem, we dealt with high and low frequency coefficient which was decomposed by wavelet by adopting different fusion rules,and an improved fusion method based on high-frequency compressed sensing of regional characteristics was proposed.Among them,low-frequency coefficients fusion method used the regional variance of weighted and maximum absolute value.Firstly,the high-frequency coefficients by random measurement matrix has better restricted isometry property compression sampling.The observed value based on energy matching degree is used for different additive or weighted fusion,to fuse the characteristic information of high frequency sub bands in different directions.Then the orthogonal matching pursuit recovery algorithm is used to to reconstruct the signal of high-frequency part.Finally,the low-frequency and high-frequency information in invert wavelet transform are used for reconstructing the fusion image.Experimental results show that compared with the previous fusion method based on compressed sensing,the effect of the fused image is more clear,new algorithm both in subjective evaluation and objective evaluation index are conducive to the image signal reconstruction,and has good usability.

Key words: Compressed sensing,Image fusion,Wavelet transform,Regional characteristics