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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Multitrack Compressed Sensing for Faster Hyperspectral Imaging.

Sharvaj Kubal1,2,3, Elizabeth Lee2, Chor Yong Tay3,4

  • 1Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore 138634, Singapore.

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Summary
This summary is machine-generated.

Hyperspectral imaging (HSI) challenges are overcome with new compressed sensing (CS) methods. Multitrack acquisition significantly speeds up data collection and reconstruction without losing accuracy.

Keywords:
adaptive imagingcompressed sensinghyperspectral imagingwavelets

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Area of Science:

  • Optics and Photonics
  • Computer Vision
  • Data Science

Background:

  • Hyperspectral imaging (HSI) offers rich spectral information beyond traditional color imaging, crucial for applications in biomedicine, materials science, and food safety.
  • However, HSI is hindered by large data volumes and lengthy acquisition times, limiting its practical use.
  • Existing compressed sensing (CS) techniques for HSI involve trade-offs between reconstruction accuracy, speed, and scene generalizability.

Purpose of the Study:

  • To develop advanced compressed sensing (CS) approaches for hyperspectral imaging (HSI) that enhance measurement speed and reconstruction efficiency.
  • To introduce a multitrack acquisition architecture for HSI, enabling parallel acquisition of multiple spectra per shot.
  • To present two novel CS algorithms compatible with the multitrack architecture: block compressed sensing and wavelet-domain adaptive sampling.

Main Methods:

  • Developed a parallelized multitrack acquisition system for HSI.
  • Implemented a sparse recovery algorithm utilizing block compressed sensing.
  • Created an adaptive CS algorithm based on wavelet domain sampling.
  • Validated the proposed methods computationally using simulated noiseless and noisy measurements.

Main Results:

  • The multitrack adaptive CS approach demonstrated a ~10-fold reduction in combined measurement and reconstruction time compared to full sampling HSI.
  • Reconstruction accuracy was maintained across tested sample images using the multitrack adaptive CS method.
  • The multitrack non-adaptive CS (sparse recovery) method exhibited superior robustness against Poisson noise, albeit with increased reconstruction times.

Conclusions:

  • The developed multitrack CS approaches significantly accelerate HSI acquisition and reconstruction while preserving image quality.
  • Multitrack adaptive CS offers a highly efficient solution for HSI, balancing speed and accuracy.
  • Multitrack non-adaptive CS provides a robust alternative for applications dealing with noisy data, particularly Poisson noise.