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Related Experiment Video

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On-Chip Compressive Sensing with a Single-Photon Avalanche Diode Array.

Chenxi Qiu1, Peng Wang1, Xiangshun Kong1

  • 1School of Electrical Science and Engineering, Nanjing University, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a compact compressive sensing single-photon avalanche diode (CS-SPAD) sensor for high-sensitivity imaging. The novel system reduces sensor size and data rates, enabling efficient spatial compressive imaging with advanced neural network processing.

Keywords:
compressed sensingefficient scene perceptionsingle-photon avalanche diode

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Area of Science:

  • Photonics and Imaging Technology
  • Computational Imaging
  • Machine Learning for Sensor Systems

Background:

  • Single-photon avalanche diodes (SPADs) offer ultra-high sensitivity for photon detection.
  • Existing SPAD arrays face challenges with large sensor area and high data throughput.
  • Need for compact, efficient imaging solutions for SPAD technology.

Purpose of the Study:

  • To develop a compact snapshot compressive sensing SPAD (CS-SPAD) sensor.
  • To enable on-chip spatial compressive imaging, reducing readout circuit area and data load.
  • To introduce a deep learning algorithm for reconstructing and classifying compressed SPAD data.

Main Methods:

  • Designed a CS-SPAD sensor with integrated circuit connections for compressive sensing.
  • Leveraged the digital counting nature of SPADs for efficient data acquisition.
  • Developed CSSPAD-Net, a convolutional neural network for high-fidelity scene reconstruction and classification.

Main Results:

  • Fabricated a prototype CS-SPAD sensor chip and imaging system.
  • Demonstrated successful on-chip snapshot compressive sensing on the MNIST dataset.
  • Achieved high-fidelity reconstruction and classification of handwritten digital images.

Conclusions:

  • The proposed CS-SPAD sensor effectively integrates compressive sensing for compact, high-sensitivity imaging.
  • CSSPAD-Net provides robust performance for reconstructing and classifying compressed SPAD data.
  • This approach significantly advances SPAD imaging by reducing hardware complexity and enhancing data processing capabilities.