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An Image-Based Quantized Compressive Sensing Scheme Using Zadoff-Chu Measurement Matrix.

Linlin Xue1, Weiwei Qiu1, Yue Wang1

  • 1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

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Summary

This study introduces a complex-valued Zadoff-Chu matrix for image compressive sensing (CS), improving reconstruction. Block compressive sensing (BCS) with optimized block size and medium-resolution quantization offers efficient, accurate image recovery.

Keywords:
Zadoff–Chu matrixblock compressive sensingquantization

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Area of Science:

  • Signal Processing
  • Image Processing
  • Information Theory

Background:

  • Compressive Sensing (CS) enables signal acquisition below the Nyquist rate.
  • Image reconstruction in CS is computationally intensive and sensitive to measurement matrix properties.
  • Quantization and block-based approaches are explored to enhance CS efficiency.

Purpose of the Study:

  • To propose and evaluate a complex-valued Zadoff-Chu measurement matrix for image-based CS.
  • To investigate the impact of block compressive sensing (BCS) and block size on reconstruction performance.
  • To analyze the effect of quantization resolution on the accuracy and feasibility of image-based BCS.

Main Methods:

  • Development of a complex-valued Zadoff-Chu measurement matrix.
  • Application of Block Compressive Sensing (BCS) with varying block sizes.
  • Simulation-based analysis of quantization effects using different analog-to-digital converter (ADC) resolutions.

Main Results:

  • The proposed Zadoff-Chu matrix outperforms traditional real-valued matrices in reconstruction.
  • Optimized block size in BCS reduces computational complexity and improves reconstruction accuracy.
  • Medium-resolution quantization achieves reconstruction performance comparable to high-resolution quantization.

Conclusions:

  • Complex-valued Zadoff-Chu matrices offer superior performance in image CS.
  • BCS with appropriate block size selection is an effective strategy for computational efficiency and accuracy.
  • Low-power image-based BCS frameworks are feasible using medium-resolution quantization.