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Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling.

Victor J Barranca1, Gregor Kovačič2, Douglas Zhou3

  • 1Department of Mathematics and Statistics, Swarthmore College, 500 College Avenue, Swarthmore, PA 19081, USA.

Scientific Reports
|August 25, 2016
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Summary
This summary is machine-generated.

Localized random sampling improves image reconstruction quality in compressive sensing (CS). This new method, inspired by visual systems, consistently yields better results than uniform random sampling across various parameters and natural images.

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Area of Science:

  • Signal Processing
  • Image Reconstruction
  • Computational Imaging

Background:

  • Compressive sensing (CS) theory highlights the impact of sampling strategies on image reconstruction quality.
  • Uniformly-random sampling often outperforms uniformly-spaced sampling in CS.
  • The structure of sampling protocols significantly influences the fidelity of reconstructed images.

Purpose of the Study:

  • To introduce a novel sampling scheme for compressive sensing (CS) image reconstruction.
  • To investigate a localized random sampling method inspired by physiological receptive field structures.
  • To demonstrate improved CS image reconstruction quality using the proposed localized random sampling.

Main Methods:

  • Developed a localized random sampling protocol where pixels and their neighbors are measured probabilistically based on distance.
  • Compared the performance of localized random sampling against uniformly-random sampling across a wide range of parameters.
  • Evaluated reconstruction quality for diverse natural images using both sampling strategies.

Main Results:

  • Localized random sampling consistently yields higher quality image reconstructions compared to uniformly-random sampling at optimal parameter choices.
  • The proposed method demonstrates stability across diverse natural images.
  • The optimal parameter choice for localized random sampling scales effectively with the number of samples.

Conclusions:

  • Localized random sampling offers a significant improvement in compressive sensing (CS) image reconstruction.
  • The findings suggest a link between receptive field structures in visual systems and efficient sampling strategies.
  • This research opens new avenues for CS theory and its application in brain imaging.