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Coded Aperture Hyperspectral Image Reconstruction.

Ignacio García-Sánchez1, Óscar Fresnedo1, José P González-Coma2

  • 1Department of Computer Engineering & CITIC Research Center, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary
This summary is machine-generated.

This study reconstructs hyperspectral images using Compressed Sensing (CS) theory and a practical block Compressed Aperture Spectral Imaging (CASSI) model. Four algorithms (OMP, GPSR, LASSO, IST) were compared for efficient hyperspectral image reconstruction.

Keywords:
CASSIcompressive sensinghyperspectral imagingsnapshot devicessparse estimation algorithmssystem evaluation

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Area of Science:

  • Optics and photonics
  • Signal processing
  • Computer vision

Background:

  • Hyperspectral imaging captures detailed spectral information across numerous bands.
  • Compressed Sensing (CS) theory offers efficient data acquisition and reconstruction strategies.
  • The Compressed Aperture Spectral Imaging (CASSI) device enables compressive sensing for hyperspectral imaging.

Purpose of the Study:

  • To analyze and compare the performance of different algorithms for hyperspectral image reconstruction from CASSI measurements.
  • To develop and evaluate a practical block CASSI model for handling large hyperspectral image dimensions.
  • To investigate the impact of block modeling and dispersive effects on reconstruction accuracy.

Main Methods:

  • Modeling the CASSI sensing procedure using Compressed Sensing (CS) theory.
  • Implementing and comparing four estimation algorithms: Orthogonal Matching Pursuit (OMP), Gradient Projection for Sparse Reconstruction (GPSR), Least Absolute Shrinkage and Selection Operator (LASSO), and Iterative Shrinkage-Thresholding (IST).
  • Developing a block CASSI model to manage computational costs and reconstruction delays for large-scale hyperspectral images.

Main Results:

  • The study evaluated the performance of OMP, GPSR, LASSO, and IST algorithms in the context of the block CASSI model.
  • Analysis focused on factors influencing the hyperspectral image reconstruction procedure within the practical CASSI setup.
  • Reconstruction performance was assessed considering the specific challenges of the block model and dispersive effects.

Conclusions:

  • The practical block CASSI model facilitates efficient hyperspectral image reconstruction.
  • Algorithm selection and parameter tuning are critical for optimizing reconstruction quality in CASSI systems.
  • The findings provide practical insights into hyperspectral image reconstruction using compressive sensing techniques.