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Image Fusion for Video Surveillance in Curvelet Domain

To simulate biological activities of human visual system, we propose a curvelet-based image fusion method using unit-linking pulse coupled neural networks (ULPCNNs) model. Contrasts of detailed coefficients are inputted into the ULPCNNs to imitate the sensitivity of HVS to detailed information, and the contrasts are also employed as corresponding linking strength for the neurons. After motivated by external stimuli from images, ULPCNNs can produce series of binary pulses containing much information of global features. Then we use the average firing times of output pulses in a neighborhood as the salience measure to determine our fusion rules. Experimental results demonstrate that, our proposed method has a satisfying fusion result both on visual effects and objective evaluations.