Enhanced deep unrolling networks for snapshot compressive hyperspectral imaging

Xinran Qin ¹, Yuhui Quan ², Hui Ji ³

⁰School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.

Neural Networks : the Official Journal of the International Neural Network Society +

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March 26, 2024

View abstract on PubMed

Summary

This study introduces EDUNet, a novel deep unrolling neural network for reconstructing hyperspectral images from compressive snapshots. EDUNet significantly improves reconstruction accuracy and convergence speed in snapshot compressive hyperspectral imaging.

Area Of Science

Computer Vision
Signal Processing
Machine Learning

Background

Snapshot compressive hyperspectral imaging (SCHI) presents a significant inverse problem for reconstructing full hyperspectral images from limited measurements.
Existing methods often struggle with reconstruction accuracy and convergence speed.

Purpose Of The Study

To propose an enhanced deep unrolling neural network (EDUNet) for accurate hyperspectral image reconstruction in SCHI.
To improve the convergence and performance of hyperspectral image reconstruction algorithms.

Main Methods

EDUNet is developed by deep unrolling a proximal gradient descent algorithm, incorporating novel gradient-driven update and proximal mapping modules.
The gradient-driven update module uses a memory-assisted descent for enhanced convergence.
The proximal mapping module features cross-stage spectral self-attention and a spectral geometry consistency loss for improved spectral information capture.

Main Results

Experiments on benchmark datasets (KAIST, ICVL, Harvard) and real data show EDUNet outperforms 15 competing models.
EDUNet achieved superior performance across PSNR, SSIM, SAM, and ERGAS metrics.
The proposed modules effectively exploit spectral self-similarities and geometric layouts.

Conclusions

EDUNet offers a robust and effective solution for hyperspectral image reconstruction in SCHI.
The novel architectural components and loss function contribute to significant performance gains.
This work advances the state-of-the-art in compressive hyperspectral imaging reconstruction.

Keywords:

Deep unrolling networks Hyperspectral imaging Image reconstruction Snapshot compressive imaging