Generalized Image Acquisition and Analysis

An evaluation of optical flow algorithms for background oriented schlieren imaging

The background oriented schlieren method (BOS) allows for accurate flow measurements with a simple experimental configuration. To estimate per-pixel displacement vectors between two images, BOS systems traditionally borrow window-based algorithms from particle image velocimetry. In this paper, we evaluate the performance of more recent optical flow methods in BOS settings. We also analyze the impact of different background patterns, suggesting the use of a pattern with detail at many scales. Experiments with both synthetic and real datasets show that the performance of BOS systems can be significantly improved through a combination of optical flow algorithms and multiscale background.

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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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