Generalized Image Acquisition and Analysis

Synchronization and Rolling Shutter Compensation for Consumer Video Camera Arrays

Two major obstacles to the use of consumer camcorders in computer vision applications are the lack of synchronization hardware, and the use of a "rolling" shutter, which introduces a temporal shear in the video volume. We present two simple approaches for solving both the rolling shutter shear and the synchronization problem at the same time. The first approach is based on strobe illumination, while the second employs a subframe warp along optical flow vectors. In our experiments we have used the proposed methods to effectively remove temporal shear, and synchronize up to 16 consumer-grade camcorders in multiple geometric configurations.

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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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