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Single-pixel quantum ghost imaging using generalized Ising model.

Hamidreza Oliaei-Moghadam1, Mahmood Hosseini-Farzad2, Najmeh TabeBordbar3

  • 1Department of Physics, College of Sciences, Shiraz University, Shiraz, 71946-84795, Iran. oliaeimoghaddam@gmail.com.

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Summary
This summary is machine-generated.

This study introduces an adaptive pattern generation method for single-pixel quantum ghost imaging (QGI) using the generalized Ising model. This novel approach reduces measurement requirements for high-quality image reconstruction.

Keywords:
Adaptive pattern generationIsing modelSingle-pixel imaging

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Area of Science:

  • Optics and photonics
  • Computational imaging
  • Quantum information science

Background:

  • Advanced single-pixel imaging systems rely on spatial light modulators (SLMs) to illuminate scenes with diverse patterns for object information gathering.
  • Effective pattern sets and reconstruction algorithms are crucial for achieving rapid, high-quality image reconstruction in these systems.

Purpose of the Study:

  • To introduce an adaptive pattern generation method for single-pixel quantum ghost imaging (QGI) systems.
  • To leverage the generalized Ising model for creating efficient illumination patterns.

Main Methods:

  • Developed an adaptive pattern generation technique based on the generalized 2D Ising model.
  • Modeled spin states (up/down) as pixel states (on/off) on the spatial light modulator (SLM).
  • Generated binary patterns that mimic the object's shape using the Ising model.

Main Results:

  • The proposed method generates binary patterns that closely resemble the target object's shape.
  • Imaging with these generated patterns can significantly reduce the number of measurements needed.
  • Achieved high-quality image reconstruction with fewer measurements compared to conventional methods.

Conclusions:

  • The generalized Ising model provides an effective framework for adaptive pattern generation in QGI.
  • This method enhances imaging efficiency by reducing measurement counts.
  • Offers a promising approach for rapid, high-quality image reconstruction in single-pixel imaging applications.