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Related Experiment Video

Updated: Nov 9, 2025

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

Single-pixel imaging of dynamic objects using multi-frame motion estimation.

Sagi Monin¹, Evgeny Hahamovich¹, Amir Rosenthal²

¹Department of Electrical Engineering, Technion - Israel Institute of Technology, 32000, Haifa, Israel.

Scientific Reports

|April 9, 2021

Summary

This summary is machine-generated.

This study introduces a novel single-pixel imaging (SPI) method to overcome motion artifacts. The new approach enables accurate imaging of dynamic scenes by estimating and correcting object movement during data acquisition.

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Last Updated: Nov 9, 2025

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Area of Science:

Optics and Photonics
Computational Imaging
Signal Processing

Background:

Single-pixel imaging (SPI) traditionally requires static scenes for accurate reconstruction.
Compressed-sensing algorithms in SPI can reduce the number of required illumination patterns.
The sequential data acquisition in SPI is a limitation for dynamic or moving objects.

Purpose of the Study:

To develop a single-pixel imaging (SPI) technique capable of imaging objects that move during data acquisition.
To enable SPI in scenarios where the imaged scene is not static.
To improve the robustness and applicability of SPI for real-world dynamic imaging.

Main Methods:

Development of algorithms to estimate object motion direction from inter-frame cross-correlations.
Incorporation of estimated motion into the SPI reconstruction model.
Exploitation of cyclic illumination patterns for direct motion estimation from raw data, enhancing numerical efficiency.

Main Results:

Successful demonstration of the motion-compensated SPI approach using both simulated and measured data.
Validation of the algorithm's ability to reconstruct images of moving objects.
Quantification of improved imaging performance in dynamic scenarios compared to traditional SPI.

Conclusions:

The presented SPI method effectively handles object motion during data acquisition, expanding its practical applications.
Motion estimation and compensation are crucial for robust SPI of dynamic scenes.
The developed algorithms offer a significant advancement for single-pixel imaging in non-static environments.