Multi-focus image fusion method based on improved VGG network

In order to better retain the original image information and improve the image fusion performance, an improved fusion algorithm combining visual geometry group(VGG) convolutional neural network with edge pixel statistical features was proposed. Firstly, this algorithm divided the complete image into image blocks, preprocessed the image blocks to obtain a higher image classification accuracy of 0.985 or more, and used the improved VGG convolutional neural network to accelerate the convergence of the model. When the image blocks were input into the network, the weight matrix of binary classification could be preliminarily obtained. Secondly, in the high-frequency detail part, the clear blur modules of left-focus image and right-focus image were respectively subjected to blurring processing, and the weights matrix with obvious boundaries was obtained by the threshold segmentation based on the statistical feature between the pixel points. Finally, combined with the two-step weight matrix, the fusion strategy of weighted sum was used to obtain the clear focus image. In order to illustrate the effectiveness of the algorithm, the experimental part shows the objective evaluation of its fusion subjective visual effect diagram and information entropy, which is outstanding compared with other algorithms and can better retain the information of the original image.