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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

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Published on: February 12, 2014

Resolution loss without imaging blur.

Tali Treibitz1, Yoav Y Schechner

  • 1Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0404, USA. tali@cs.ucsd.edu

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|December 4, 2012
PubMed
Summary
This summary is machine-generated.

This study analyzes how object size affects image recovery under noise. Low signal-to-noise ratio (SNR) causes resolution loss, similar to image blur, impacting object detection and recovery success rates.

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Area of Science:

  • Image processing
  • Computational imaging
  • Signal processing

Background:

  • Image recovery under noise is a well-studied problem.
  • Performance analysis often overlooks the impact of object size.
  • Understanding object size influence is crucial for practical imaging applications.

Purpose of the Study:

  • To analyze image recovery probability as a function of object spatial frequency.
  • To investigate the impact of signal-to-noise ratio (SNR) on resolution loss.
  • To develop a tool for assessing object recovery capability based on various imaging parameters.

Main Methods:

  • Utilized a physical model for signal and noise acquisition.
  • Incorporated post-acquisition noise filtering into the analysis.
  • Employed linear-systems analysis to determine effective cutoff frequency.
  • Considered pointwise image formation models (e.g., scattering, attenuation).

Main Results:

  • Identified an effective cutoff frequency induced by noise, mimicking optical blur.
  • Demonstrated that low SNR leads to resolution loss, irrespective of optical blur.
  • Quantified the influence of object size, distance, radiance difference, and medium attenuation on recovery success.

Conclusions:

  • Noise-induced cutoff frequency limits image recovery, similar to optical blur.
  • Object size and imaging conditions significantly affect the probability of successful image recovery.
  • The developed tool provides bounds for object recovery based on camera specifications and environmental factors.