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HCNet: Multi-Exposure High-Dynamic-Range Reconstruction Network for Coded Aperture Snapshot Spectral Imaging.

Hang Shi1,2, Jingxia Chen1, Yahui Li2

  • 1School of Electronics Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China.

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Summary
This summary is machine-generated.

This study introduces a multi-exposure fusion method to enhance Coded Aperture Snapshot Spectral Imaging (CASSI) reconstruction quality. The new approach overcomes dynamic range limitations, improving hyperspectral image fidelity in challenging high-dynamic-range scenes.

Keywords:
compressed measurement reconstructionhigh dynamic rangemulti-exposure fusionsnapshot compressive spectral imaging

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Area of Science:

  • Optical Engineering
  • Image Processing
  • Spectroscopy

Background:

  • Coded Aperture Snapshot Spectral Imaging (CASSI) is a rapid hyperspectral imaging technique.
  • Existing CASSI systems face limitations due to finite dynamic range in compressed measurements, degrading reconstruction quality.
  • Hardware constraints and data acquisition modes contribute to these dynamic range issues.

Purpose of the Study:

  • To develop a high-quality hyperspectral reconstruction method addressing the dynamic range limitations of CASSI.
  • To improve the fidelity of hyperspectral data reconstruction in both spatial and spectral dimensions.
  • To enhance CASSI's measurement capability in high-dynamic-range (HDR) scenarios.

Main Methods:

  • A multi-exposure data acquisition strategy capturing low-, medium-, and high-exposure low-dynamic-range (LDR) measurements.
  • Design of a multi-exposure fusion-based high-dynamic-range (HDR) CASSI measurement reconstruction network (HCNet).
  • HCNet utilizes a multiscale feature fusion architecture with local-global convolutional joint attention and residual enhancement for fusing multi-exposure information.

Main Results:

  • The proposed method significantly improves hyperspectral image reconstruction quality compared to single-exposure strategies.
  • HCNet demonstrates high robustness against multi-exposure interval jitters and shot noise in practical CASSI systems.
  • Reconstruction shows enhanced contrast in both bright and dark details and higher spectral correlation, especially in HDR scenes.

Conclusions:

  • The multi-exposure fusion approach effectively reconstructs physically consistent HDR CASSI measurements.
  • The developed HCNet is well-suited for CASSI systems, enabling high-fidelity hyperspectral data reconstruction.
  • This method validates enhanced CASSI reconstruction and effective measurement capability in HDR scenarios.