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.


Three-Dimensional Kaleidoscopic Imaging

Ilya Reshetouski, Alkhazur Manakov, Hans-Peter Seidel, Ivo Ihrke
In: Proceedings of CVPR 2011 (oral).


Three-dimensional kaleidoscopic imaging, a promising alternative for recording multi-view imagery. The main limitation of multi-view reconstruction techniques is the limited number of views that are available from multi-camera systems, especially for dynamic scenes. Our new system is based on imaging an object inside a kaleidoscopic mirror system. We show that this approach can generate a large number of high-quality views well distributed over the hemisphere surrounding the object in a single shot. In comparison to existing multi-view systems, our method offers a number of advantages: it is possible to operate with a single camera, the individual views are perfectly synchronized, and they have the same radiometric and colorimetric properties. We describe the setup both theoretically, and provide methods for a practical implementation. Enabling interfacing to standard multi-view algorithms for further processing is an important goal of our techniques.
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author = {Ilya Reshetouski and Alkhazur Manakov and Hans-Peter Seidel and Ivo Ihrke},
title = {Three-Dimensional Kaleidoscopic Imaging},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = 2011,
pages = {353--360},
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