Multifocus image fusion using convolutional neural network

Acquiring all-in-focus images is significant in the multi-media era. Limited by the depth-of-field of the optical lens, it is hard to acquire an image with all targets are clear. One possible solution is to merge the information of a few complementary images in the same scene. In this article, we em...

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Bibliographic Details
Published in:Multimedia tools and applications 2020-12, Vol.79 (45-46), p.34531-34543
Main Authors: Wen, Yu, Yang, Xiaomin, Celik, Turgay, Sushkova, Olga, Albertini, Marcelo Keese
Format: Article
Language:English
Subjects:
Artificial neural networks
Clarity
Computer Communication Networks
Computer Science
Computer vision
Data Structures and Information Theory
Decomposition
Depth of field
Image acquisition
Image processing
Morphology
Multimedia
Multimedia Information Systems
Neural networks
Principal components analysis
Special Purpose and Application-Based Systems
Target recognition
Wavelet transforms
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Summary:Acquiring all-in-focus images is significant in the multi-media era. Limited by the depth-of-field of the optical lens, it is hard to acquire an image with all targets are clear. One possible solution is to merge the information of a few complementary images in the same scene. In this article, we employ a two-channel convolutional network to derive the clarity map of source images. Then, the clarity map is smoothed by using morphological filtering. Finally, the fusion image is constructed via merging the clear parts of source images. Experimental results prove that our approach has a better performance on both visual quality and quantitative evaluations than many previous fusion approaches.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-08945-z