Abstract:

A novel algorithm for image fusion was proposed based on sparse representation and PCNN (pulse coupled neural network). The bandelet transform was used to extract important information such as geometric flows and bandelet coefficients of the source image.Then geometric flows were fused by PCNN and optimized according to similarity of sparseness. Then, the  bandelet coefficients were updated and fused according to a rule of maximum absolute. Finally, the  inverse bandelet transform was applied for the fused image. The experimental results  showed that this algorithm could  effectively improve the fusion effect. The fusion image had clear edges, texture and excellent overall effect. Compared with the average algorithm, the Laplace pyramid algorithm and the WT-PCNN algorithm  based on wavelet transform and PCNN,  a proposed algorithm achieved the better average gray, standard deviation, average gradient and mutual information.

Key words: sparse representation for signal, geometry flow, pulse coupled neural network, image fusion, bandelet transform