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Fast lapped block reconstructions in compressive spectral imaging.

Henry Arguello1, Claudia V Correa, Gonzalo R Arce

  • 1Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware 19716-3130, USA.

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This study introduces lapped block reconstruction for coded aperture snapshot spectral imagers (CASSI), significantly accelerating 3D data cube reconstruction. The new method achieves faster processing while maintaining or improving image quality.

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Area of Science:

  • Optics and Photonics
  • Computational Imaging
  • Signal Processing

Background:

  • Coded aperture snapshot spectral imagers (CASSI) capture spatial and spectral information via random projections.
  • Reconstructing 3D scenes from CASSI measurements typically uses iterative algorithms like GPSR.
  • High computational complexity (O(KN4L)) limits real-time applications of CASSI.

Purpose of the Study:

  • To develop a computationally efficient reconstruction method for CASSI.
  • To reduce the overwhelming computational load in 3D spectral data cube reconstruction.
  • To maintain or enhance image quality while accelerating the reconstruction process.

Main Methods:

  • Introduced a mathematical model for lapped block reconstructions in CASSI.
  • Leveraged the sensing matrix structure for independent recovery of overlapping blocks.
  • Merged reconstructed 3D blocks to mitigate artifacts and form the full data cube.

Main Results:

  • Achieved a reduced computational complexity of O(KB4L) per iteration for block reconstruction.
  • Demonstrated an overall reconstruction complexity of O(K(N4/(N')2)L) per iteration.
  • Accelerated data cube reconstruction by an order of magnitude compared to traditional methods.

Conclusions:

  • The lapped block reconstruction model offers significant speedups for CASSI data processing.
  • The method provides comparable or superior image reconstruction quality.
  • This advancement makes CASSI more viable for demanding applications requiring rapid spectral imaging.