@inproceedings{904bf97853124de6bf94e182907d25a1,
title = "HSCNN: CNN-Based Hyperspectral Image Recovery from Spectrally Undersampled Projections",
abstract = "This paper presents a unified deep learning framework to recover hyperspectral images from spectrally undersampled projections. Specifically, we investigate two kinds of representative projections, RGB and compressive sensing (CS) measurements. These measurements are first upsampled in the spectral dimension through simple interpolation or CS reconstruction, and the proposed method learns an end-to-end mapping from a large number of up-sampled/groundtruth hyperspectral image pairs. The mapping is represented as a deep convolutional neural network (CNN) that takes the spectrally upsampled image as input and outputs the enhanced hyperspetral one. We explore different network configurations to achieve high reconstruction fidelity. Experimental results on a variety of test images demonstrate significantly improved performance of the proposed method over the state-of-the-arts.",
author = "Zhiwei Xiong and Zhan Shi and Huiqun Li and Lizhi Wang and Dong Liu and Feng Wu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 ; Conference date: 22-10-2017 Through 29-10-2017",
year = "2017",
month = jul,
day = "1",
doi = "10.1109/ICCVW.2017.68",
language = "English",
series = "Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "518--525",
booktitle = "Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017",
address = "United States",
}