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Proceedings Paper

Super-resolved all-refocused image with a plenoptic camera
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Paper Abstract

This paper proposes an approach to produce the super-resolution all-refocused images with the plenoptic camera. The plenoptic camera can be produced by putting a micro-lens array between the lens and the sensor in a conventional camera. This kind of camera captures both the angular and spatial information of the scene in one single shot. A sequence of digital refocused images, which are refocused at different depth, can be produced after processing the 4D light field captured by the plenoptic camera. The number of the pixels in the refocused image is the same as that of the micro-lens in the micro-lens array. Limited number of the micro-lens will result in poor low resolution refocused images. Therefore, not enough details will exist in these images. Such lost details, which are often high frequency information, are important for the in-focus part in the refocused image. We decide to super-resolve these in-focus parts. The result of image segmentation method based on random walks, which works on the depth map produced from the 4D light field data, is used to separate the foreground and background in the refocused image. And focusing evaluation function is employed to determine which refocused image owns the clearest foreground part and which one owns the clearest background part. Subsequently, we employ single image super-resolution method based on sparse signal representation to process the focusing parts in these selected refocused images. Eventually, we can obtain the super-resolved all-focus image through merging the focusing background part and the focusing foreground part in the way of digital signal processing. And more spatial details will be kept in these output images. Our method will enhance the resolution of the refocused image, and just the refocused images owning the clearest foreground and background need to be super-resolved.

Paper Details

Date Published: 14 December 2015
PDF: 8 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130N (14 December 2015); doi: 10.1117/12.2205858
Show Author Affiliations
Xiang Wang, Beijing Institute of Technology (China)
Lin Li, Beijing Institute of Technology (China)
Guangqi Hou, Institute of Automation (China)

Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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