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Separable Coded Aperture for Depth from a Single Image

delete2014-12-01
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PRE
AI
J
Jingyu Lin *
X
Xing Lin
季
季向阳 (Xiangyang Ji)
戴
戴琼海 (Qionghai Dai)
DOI:10.1109/LSP.2014.2343251delete
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Abstract

Abstract

En 中文
We propose the use of a separable coded aperture to estimate depth from a single defocused image, and derive a criterion for evaluating aperture patterns with respect to depth discrimination. With a separable coded aperture, two-dimensional (2D) point spread functions (PSFs) are separated into the product of horizontal and vertical one-dimensional (1D) PSFs. Depth is recovered by finding the scale of the 1D PSFs at each pixels based on the space-frequency analysis. Benefiting from 1D PSFs and the optimal code, our approach obtains more accurate depth especially on depth boundaries, and is faster since 2D computation is reduced to 1D. Experimental results on synthetic and real-captured data demonstrates the effectiveness of our approach.
Keywords:
Coded aperture
depth boundary
depth estimation
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Signal Processing Magazine cover
IEEE Signal Processing Magazine
IF:
9.6
Papers:
1.1W
Citations:
1.7W

Organization

T
tsinghua university
Scholars:
11.9W
Papers: 10.0W
Citations: 137
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