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Feasibility of a Real-Time Embedded Hyperspectral Compressive Sensing Imaging System.

Olivier Lim1,2, Stéphane Mancini1, Mauro Dalla Mura2,3

  • 1University Grenoble Alpes, CNRS, Grenoble INP, TIMA, 38031 Grenoble, France.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

Compressive hyperspectral imaging reconstructs data faster using hardware acceleration and optimized algorithms. This enables real-time applications by addressing computational bottlenecks in image reconstruction.

Keywords:
CGNEDD CASSIcompressive sensingcomputation complexityembedded systemsfield-programmable gate array (FPGA)graphics processing unit (GPU)hyperspectral imagingremote sensing

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Area of Science:

  • Optics and Photonics
  • Computer Science
  • Signal Processing

Background:

  • Hyperspectral imaging (HSI) offers rich spectral data for diverse applications like remote sensing and medicine.
  • Compressive HSI systems enable data acquisition with fewer samples than traditional methods.
  • Reconstruction algorithms are crucial for recovering data in compressive HSI but are computationally intensive.

Purpose of the Study:

  • To analyze the performance requirements for real-time compressive hyperspectral imaging.
  • To identify strategies for accelerating HSI data reconstruction.
  • To assess the feasibility of real-time applications for compressive HSI systems.

Main Methods:

  • Analysis of computational power, memory, and bandwidth needs for a state-of-the-art reconstruction algorithm.
  • Evaluation of algorithmic and hardware acceleration techniques (GPUs, FPGAs).
  • Investigation of system matrix sparsity and data encoding for bandwidth reduction.

Main Results:

  • Real-time reconstruction for compressive hyperspectral imaging is achievable.
  • Exploiting system matrix sparsity significantly reduces computational load.
  • Optimized data value encoding minimizes bandwidth requirements.

Conclusions:

  • Compressive hyperspectral imaging can achieve real-time performance through combined acceleration strategies.
  • Hardware acceleration and algorithmic optimizations are key to overcoming reconstruction bottlenecks.
  • This work paves the way for wider adoption of compressive HSI in time-sensitive applications.