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Proceedings Paper

Ghost detection and removal based on super-pixel grouping in exposure fusion
Author(s): Shenyu Jiang; Zhihai Xu; Qi Li; Yueting Chen; Huajun Feng
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Paper Abstract

A novel multi-exposure images fusion method for dynamic scenes is proposed. The commonly used techniques for high dynamic range (HDR) imaging are based on the combination of multiple differently exposed images of the same scene. The drawback of these methods is that ghosting artifacts will be introduced into the final HDR image if the scene is not static. In this paper, a super-pixel grouping based method is proposed to detect the ghost in the image sequences. We introduce the zero mean normalized cross correlation (ZNCC) as a measure of similarity between a given exposure image and the reference. The calculation of ZNCC is implemented in super-pixel level, and the super-pixels which have low correlation with the reference are excluded by adjusting the weight maps for fusion. Without any prior information on camera response function or exposure settings, the proposed method generates low dynamic range (LDR) images which can be shown on conventional display devices directly with details preserving and ghost effects reduced. Experimental results show that the proposed method generates high quality images which have less ghost artifacts and provide a better visual quality than previous approaches.

Paper Details

Date Published: 2 September 2014
PDF: 6 pages
Proc. SPIE 9284, 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronics Materials and Devices for Sensing and Imaging, 928418 (2 September 2014); doi: 10.1117/12.2070620
Show Author Affiliations
Shenyu Jiang, Zhejiang Univ. (China)
Zhihai Xu, Zhejiang Univ. (China)
Qi Li, Zhejiang Univ. (China)
Yueting Chen, Zhejiang Univ. (China)
Huajun Feng, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 9284:
7th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronics Materials and Devices for Sensing and Imaging
Yadong Jiang; Junsheng Yu; Bernard Kippelen, Editor(s)

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