Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6856722 | Information Sciences | 2018 | 23 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Tang Han, Xiao Bin, Li Weisheng, Wang Guoyin,