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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Published on: February 12, 2014

Phase diversity with undersampled systems via superresolution preprocessing.

Eric A Shields¹

¹Sandia National Laboratories, P. O. Box 5800, Albuquerque, New Mexico 87185-0406, USA. eashiel@sandia.gov

|June 30, 2012

Summary

This summary is machine-generated.

Phase diversity algorithms reconstruct wavefronts using through-focus images. A new method uses superresolution for undersampled remote sensing systems, enabling wavefront reconstruction even with limited sampling.

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Area of Science:

Optical Engineering
Remote Sensing
Image Processing

Background:

Phase diversity algorithms reconstruct wavefronts from through-focus measurements.
Traditional algorithms require Nyquist-sampled data, limiting their use in undersampled systems.
Remote sensing optical systems are often undersampled.

Purpose of the Study:

To adapt phase diversity algorithms for undersampled optical systems.
To enable wavefront reconstruction in remote sensing applications with limited sampling.
To demonstrate a novel approach combining superresolution with phase diversity.

Main Methods:

Employed superresolution techniques to create properly sampled scenes from undersampled data.
Applied phase diversity algorithms to the super-resolved scenes.
Experimentally validated the approach using a point object.

Main Results:

Successfully demonstrated wavefront reconstruction for an undersampled system.
Showcased the effectiveness of superresolution in preparing data for phase diversity.
The method is applicable to both point sources and extended scenes.

Conclusions:

Phase diversity algorithms can be successfully applied to undersampled systems using superresolution.
This approach expands the applicability of phase diversity in remote sensing.
Superresolution is a viable technique for overcoming sampling limitations in wavefront reconstruction.