Abstract: Multi-focus image fusion uses many complementary information of the image to obtain a clear fused image.In traditional multi-scale analysis methods,image information is easily lost due to sampling and fusion strategies.In sparse representation methods,due to the lack of dictionary expression ability,the fusion details are blurred and the fusion time complexity is very high.For multi-focus image fusion method based on spatial domain method,the algorithm for measuring image activity level is very critical.A fractional differential feature is proposed to measure the activity level of the image.The algorithm first convolves the image with a fractional mask in eight directions,and then accumulates the absolute value after convolution in each direction to obtain the activity level measurement of the original image.Each metric map is then compared separately by using a sliding window technique.The sum of the windows and the large ones is regarded as the focus,and the corresponding score map is incremented by one.The decision map is obtained by the score map information.Finally,the final fused image is obtained by weighting the original image by decision graph.Through experimental comparison and analysis,this algorithm has certain advantages over traditional algorithm.

Key words: Activity level, Fractional differential, Multi-focus image fusion, Sliding window