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Single-Pixel Multispectral Imaging With Customized Pixelated Spectral Mask Plate

  • Ziyang Chen
  • , Gang Qu
  • , Zijun He
  • , Lulu Xue
  • , Qiaolong Qian
  • , Hongyan Shi*
  • , Weiqiang Ding*
  • , Xin Yuan*
  • *Corresponding author for this work
  • School of Physics, Harbin Institute of Technology
  • Westlake University
  • The 8th Research Academy of Cssc
  • QIANYUAN National Laboratory
  • Westlake Institute for Optoelectronics

Research output: Contribution to journalArticlepeer-review

Abstract

Single-pixel imaging (SPI) has emerged as a cutting-edge computational technique, offering distinct advantages over conventional methods, particularly in non-visible wavelengths. This study introduces a compact and low-cost single-pixel multispectral imaging (SP-MSI) system that utilizes a customized pixelated spectral mask plate (PSMP) for efficient spectral modulation. By employing pre-calibrated multispectral patterns, our method enables the reconstruction of a 3D datacube ((Formula presented.)) at sampling ratios below 30%. This is achieved through a custom-designed deep unfolding network (DUN) for image reconstruction. We validate the performance of our system through comprehensive theoretical analysis and experimental demonstrations across eight spectral bands. The proposed approach presents a promising solution for developing efficient and compact multispectral and hyperspectral imaging systems, compatible with both single-pixel detectors and conventional 2D sensors.

Original languageEnglish
JournalLaser and Photonics Reviews
DOIs
StateAccepted/In press - 2026
Externally publishedYes

Keywords

  • deep learning
  • single-pixel imaging
  • spectral imaging
  • spectral mask

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