@inproceedings{42116b193d434e468c1731283194ac43,
title = "Computational Hyperspectral Imaging with Diffractive Optics and Deep Residual Network",
abstract = "Hyperspectral imaging critically serves for various fields such as remote sensing, biomedical and agriculture. Its potential can be exploited to a greater extent when combined with deep learning methods, which improve the reconstructed hyperspectral image quality and reduce the processing time. In this paper, we propose a novel snapshot hyperspectral imaging system using optimized diffractive optical element and color filter along with the residual dense network. We evaluate our method through simulations considering the effects of each optical element and noise. Simulation results demonstrate high-quality hyperspectral image reconstruction capabilities through the proposed computational hyperspectral camera.",
keywords = "Hyper-spectral Imaging, Computational Imaging, Deep Learning, Residual Learning",
author = "Ayoung Kim and Ugur Akpinar and Erdem Sahin and Atanas Gotchev",
year = "2022",
doi = "10.1109/EUVIP53989.2022.9922696",
language = "English",
series = "European Workshop on Visual Information Processing",
booktitle = "IEEE European Workshop on Visual Information Processing (EUVIP)",
note = "European Workshop on Visual Information Processing ; Conference date: 11-09-2022 Through 14-09-2022",
}