Generalized Image Acquisition and Analysis

A Theory of Plenoptic Multiplexing

Multiplexing is a common technique for encoding highdimensional image data into a single, two-dimensional image. Examples of spatial multiplexing include Bayer patterns to capture color channels, and integral images to encode light fields. In the Fourier domain, optical heterodyning has been used to acquire light fields. In this paper, we develop a general theory of multiplexing the dimensions of the plenoptic function onto an image sensor. Our theory enables a principled comparison of plenoptic multiplexing schemes, including noise analysis, as well as the development of a generic reconstruction algorithm. The framework also aides in the identification and optimization of novel multiplexed imaging applications.

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Sensor Saturation in Fourier Multiplexed Imaging

Gordon Wetzstein, Ivo Ihrke, Wolfgang Heidrich
In: Proceedings of CVPR 2010.



Abstract

Optically multiplexed image acquisition techniques have become increasingly popular for encoding different exposures, color channels, light-fields, and other properties of light onto two-dimensional image sensors. Recently, Fourier-based multiplexing and reconstruction approaches have been introduced in order to achieve a superior light transmission of the employed modulators and better signal-to-noise characteristics of the reconstructed data. We show in this paper that Fourier-based reconstruction approaches suffer from severe artifacts in the case of sensor saturation, i.e. when the dynamic range of the scene exceeds the capabilities of the image sensor. We analyze the problem, and propose a novel combined optical light modulation and computational reconstruction method that not only suppresses such artifacts, but also allows us to recover a wider dynamic range than existing image-space multiplexing approaches.
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Bibtex

@InProceedings{Wetzstein:10,
author = {G. Wetzstein and I. Ihrke and W. Heidrich},
title = {{Sensor Saturation in Fourier Multiplexed Imaging}},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun},
year = {2010}
}
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