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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：J. Electron. Imaging
摘要：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. © 2019 SPIE and IS&T.
ISSN号：1017-9909
是否译文：否
发表时间：2019-03-01
合写作者：Zhai, Hao
通讯作者：庄毅