期刊
INFORMATION SCIENCES
卷 433, 期 -, 页码 125-141出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2017.12.043
关键词
Multi-focus image fusion; Convolutional neural network; Deep learning; Focus measure
资金
- National Natural Science Foundation of China [61572092]
- Key Programme of NSFC-Guangdong Union Foundation [U1401252]
- Scientific & Technological Research Program of Chongqing Municipal Education Commission [KJ1400429]
This paper proposes a pixel-wise convolutional neural network (p-CNN) that can recognize the focused and defocused pixels in source images from its neighbourhood information for multi-focus image fusion. The proposed p-CNN can be thought of as a learned focus measure (FM) and provides more efficiency than conventional handcrafted FMs. To enable the p-CNN with the strong capability to discriminate focused and defocused pixels, a comprehensive training image set based on a public image database is created. Furthermore, by setting precise labels according to different focus levels and adding various defocus masks, the p-CNN can accurately measure the focus level of each pixel in source images in which the artefacts in the fused image can be efficiently avoided. We also propose a method to implement the p-CNN with a conventional image convolutional neural network (image-wised CNN), which is almost 25 times faster than directly using the p-CNN in multi-focus image fusion. Experimental results demonstrate that the proposed method is competitive with or even outperforms the state-of-the-art methods in terms of both subjective visual perception and objective evaluation metrics. (C) 2017 Elsevier Inc. All rights reserved.
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