Sensor Saturation in Fourier Multiplexed Imaging
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.
Three-Dimensional Kaleidoscopic ImagingIlya Reshetouski, Alkhazur Manakov, Hans-Peter Seidel, and Ivo Ihrke
CVPR 2011 (oral)
We introduce 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.
Example of labeling process:
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|Labeling of views|
Supplemental materials [pdf]
Labeling data example (with MatLab loader) [zip]
Labeling of the dynamic scene example: Input movie [mpg], Segmentation movie [mpg], Labeling movie [mpg]