Multifocus image fusion method based on a convolutional neural network

Abstract

The aim of multifocus image fusion technology is to produce an all-in-focus image, in which clear parts of different source images are integrated to a single image. Traditional image fusion methods usually suffer from some problems, such as block artifacts, artificial edges, halo effects, contrast reduction, and sharpness reduction. To address these problems, a multifocus image fusion method based on a convolutional neural network (CNN) is proposed. First, the CNN is trained using a large number of multifocus image samples to obtain a model that can correctly distinguish between clear and blurred pixels. Then the sharpness of the image to be detected is predicted using the model to form a focus map. After small-region filtering and guided filtering, a final decision map is formed. Finally, the multifocus source images are fused into a fully focused image according to the final decision map. Experimental results show that the proposed image fusion method outperforms other ones in terms of visual effects and objective evaluation.