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Focused-Region Segmentation for Refocusing Images from Light Fields

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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.

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Acknowledgements

This work is supported partly by National Natural Science Foundation of China (No. 51709083, No. 61671201), the Fundamental Research Funds for the Central Universities (No. 2018B16514), and the Natural Science Foundation of Jiangsu Higher Education Institutions of China (No. 17KJB520010).

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Authors and Affiliations

  1. College of Computer and Information Engineering, Hohai University, Nanjing, 211100, China

    Jie Shen, Lei Han, Zhen Zhang & Huibin Wang

  2. School of Computer Engineering, Nanjing Institute of Technology, Nanjing, 211167, China

    Lei Han, Mengxi Xu & Chenrong Huang

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  1. Jie Shen
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  2. Lei Han
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  3. Mengxi Xu
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  4. Chenrong Huang
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  5. Zhen Zhang
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Correspondence to Jie Shen.

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Shen, J., Han, L., Xu, M. et al. Focused-Region Segmentation for Refocusing Images from Light Fields. J Sign Process Syst 90, 1281–1293 (2018). https://doi.org/10.1007/s11265-018-1379-2

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  • DOI: https://doi.org/10.1007/s11265-018-1379-2

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