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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

The farthest point strategy for progressive image sampling.

Y Eldar¹, M Lindenbaum, M Porat

¹IBM Israel Sci. and Technol. Center, Haifa.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1997

Summary

A novel farthest point strategy (FPS) offers uniform sparse image sampling. This deterministic method provides anti-aliasing properties superior to stochastic approaches for efficient image acquisition and display.

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Area of Science:

Computer Vision
Image Processing
Computational Geometry

Background:

Progressive image acquisition requires efficient sampling strategies.
Existing stochastic methods lack deterministic uniformity guarantees.
Sparse image sampling presents challenges in maintaining image quality.

Purpose of the Study:

Introduce a new deterministic method for progressive image acquisition.
Ensure uniform sampling point distribution for improved image approximation.
Develop an efficient algorithm for sparse image sampling and display.

Main Methods:

Farthest Point Strategy (FPS) for deterministic min-max uniformity.
Irregularly spaced sampling points with anti-aliasing properties.
Modification for image-dependent adaptive sampling.
O(N log N) algorithm development.

Main Results:

FPS achieves uniform sampling point distribution even at increased density.
Demonstrates anti-aliasing properties comparable to Poisson disk sampling.
A modified FPS enables adaptive sampling schemes.
An efficient O(N log N) algorithm is presented.

Conclusions:

FPS provides a deterministic and uniform approach to sparse image sampling.
The method offers advantages over stochastic techniques for progressive image acquisition.
FPS is suitable for efficient sparse image sampling, display, and adaptive applications.