Focused-Region Segmentation for Refocusing Images from Light Fields

Focused-Region Segmentation for Refocusing Images from Light Fields Since the focused regions of the light-field refocusing image contain depth cues, focused-region segmentation becomes a fundamental step of depth estimation, 3D measurement, and visual measurement. However, in the emerging field of light-field image processing, recent research has emphasized focus evaluation rather than a systematic method for focused-region segmentation. The segmentation algorithms for low depth-of-field images are of significance for this problem, but those algorithms have high time complexity and are not suitable for the computationally intensive applications of light-field imaging. Therefore, based on the pulse synchronous mechanism of the pulse coupled neural network (PCNN), we establish the model of neural firing sequence and some criteria of pixel classification. Further, we design an algorithm of the focused-region segmentation and its parameter settings. The experimental results show that the proposed method segments the refocusing images faster than alternative methods and meets the needs of light-field image processing and related applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Signal Processing Systems Springer Journals

Focused-Region Segmentation for Refocusing Images from Light Fields

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Engineering; Signal,Image and Speech Processing; Circuits and Systems; Electrical Engineering; Image Processing and Computer Vision; Pattern Recognition; Computer Imaging, Vision, Pattern Recognition and Graphics
ISSN
1939-8018
eISSN
1939-8115
D.O.I.
10.1007/s11265-018-1379-2
Publisher site
See Article on Publisher Site

Abstract

Since the focused regions of the light-field refocusing image contain depth cues, focused-region segmentation becomes a fundamental step of depth estimation, 3D measurement, and visual measurement. However, in the emerging field of light-field image processing, recent research has emphasized focus evaluation rather than a systematic method for focused-region segmentation. The segmentation algorithms for low depth-of-field images are of significance for this problem, but those algorithms have high time complexity and are not suitable for the computationally intensive applications of light-field imaging. Therefore, based on the pulse synchronous mechanism of the pulse coupled neural network (PCNN), we establish the model of neural firing sequence and some criteria of pixel classification. Further, we design an algorithm of the focused-region segmentation and its parameter settings. The experimental results show that the proposed method segments the refocusing images faster than alternative methods and meets the needs of light-field image processing and related applications.

Journal

Journal of Signal Processing SystemsSpringer Journals

Published: May 27, 2018

References

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