research-article

Multi-focus Image Fusion with Structure-Driven Adaptive Regions

Authors:

Xiaobo Qu,

Lei Li,

Di GuoAuthors Info & Claims

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

Pages 86 - 89

https://doi.org/10.1145/2632856.2632914

Published: 10 July 2014 Publication History

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Abstract

Due to the limited length of depth in camera, sometimes it is hard to capture a scenario focusing on all objects. This problem can be solved by taking multiple images, focusing on different parts, and fusing them into one image. How to select the pixels, which lies in the focus depth, with higher clarity, is still open. In this paper, a multi-focus image fusion method with structure-driven adaptive regions is proposed. By making use of the shared self-similarity in images, adaptive regions are estimated and the charities in these regions are counted. Experimental results show that the proposed approach preserves images edges better than image fusion methods with fixed size regions and the compared multi-scale decomposition domain image fusion methods.

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Cited By

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Deshmukh VKhaparde AShaikh S(2017)Multi-focus Image Fusion Using Deep Belief NetworkInformation and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 110.1007/978-3-319-63673-3_28(233-241)Online publication date: 8-Aug-2017
https://doi.org/10.1007/978-3-319-63673-3_28

Index Terms

Multi-focus Image Fusion with Structure-Driven Adaptive Regions
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

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Information & Contributors

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Published In

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

July 2014

430 pages

ISBN:9781450328104

DOI:10.1145/2632856

General Chairs:
Hanzi Wang
Xiamen University, China
,
Larry Davis
University of Maryland, USA
,
Program Chairs:
Wenwu Zhu
Tsinghua University, China
,
Stephan Kopf
University of Mannheim, Germany
,
Yanyun Qu
Xiamen University, China
,
Publications Chairs:
Jun Yu
Xiamen University, China
,
Jitao Sang
Institute of Automation, CAS, China
,
Tao Mei
Microsoft Research Asia, China

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In-Cooperation

NSF of China: National Natural Science Foundation of China
Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

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ICIMCS '14

ICIMCS '14: International Conference on Internet Multimedia Computing and Service

July 10 - 12, 2014

Xiamen, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Deshmukh VKhaparde AShaikh S(2017)Multi-focus Image Fusion Using Deep Belief NetworkInformation and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 110.1007/978-3-319-63673-3_28(233-241)Online publication date: 8-Aug-2017
https://doi.org/10.1007/978-3-319-63673-3_28

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Multi-focus image fusion through pixel-wise voting and morphology

Multi-focus image fusion based on L1 image transform

Multi-focus image fusion based on depth extraction with inhomogeneous diffusion equation