Deep Learning Model-Based for Image Reconstruction in Compressive Spectral Imaging
| Autor: | Samuel Pinilla, Henry Arguello, Yesid Fonseca, Kevin Arias |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Artificial neural network Computer science business.industry Noise reduction Deep learning Iterative reconstruction 030204 cardiovascular system & hematology Inverse problem Residual Convolutional neural network Spectral imaging 03 medical and health sciences 0302 clinical medicine medicine Computer vision Artificial intelligence business |
| Zdroj: | Imaging and Applied Optics Congress. |
| DOI: | 10.1364/3d.2020.jth2a.35 |
| Popis: | A state-of-the-art deep learning framework, HyperReconNet, recover an spectral image from its compressed measurements. However, HyperReconNet does not take the sensing matrix into a account on the training. We propose a residual modbased convolutional neural network to address this limitation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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