ADMM-Inspired Reconstruction Network for Compressive Spectral Imaging
| Autor: | Takayuki Kurozumi, Yoko Sogabe, Hideaki Kimata, Shiori Sugimoto |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Iterative method Computer science Hyperspectral imaging 020206 networking & telecommunications 02 engineering and technology Iterative reconstruction Spectral imaging Compressed sensing Computer Science::Computer Vision and Pattern Recognition Convex optimization 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Convex function Algorithm |
| Zdroj: | ICIP |
| DOI: | 10.1109/icip40778.2020.9190998 |
| Popis: | Compressive spectral imaging (CSI) is an emerging technique for acquiring hyperspectral images based on compressed sensing theory. By combining powerful data-driven methods with traditional iterative convex optimization algorithms, we propose a multi-staged neural network architecture, which is inspired by ADMM, to achieve fast and accurate reconstruction for CSI. All parameters of ADMM and deep hyperspectral priors are jointly learned. The proposed ADMM-inspired network was experimentally shown to outperform an existing state-of-the-art reconstruction method in terms of both speed and accuracy. |
| Databáze: | OpenAIRE |
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