Name
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. (C) 2019 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/1.JEI.28.2.023018 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
multifocus image fusion,convolutional neural network,deep learning,guided filtering | Computer vision,Pattern recognition,Image fusion,Computer science,Convolutional neural network,Artificial intelligence | Journal |
Volume | Issue | ISSN |
28 | 2 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||