Dataset for multispectral illumination estimation using deep unrolling network

Li, Yuqi; Fu, Qiang; Heidrich, Wolfgang

dc.contributor.author	Li, Yuqi
dc.contributor.author	Fu, Qiang
dc.contributor.author	Heidrich, Wolfgang
dc.date.accessioned	2021-08-03T05:35:58Z
dc.date.available	2021-08-03T05:35:58Z
dc.date.issued	2021-08-02
dc.identifier.citation	Li, Y., Fu, Q., & Heidrich, W. (2021). Dataset for multispectral illumination estimation using deep unrolling network [Data set]. KAUST Research Repository. https://doi.org/10.25781/KAUST-6930V
dc.identifier.doi	10.25781/KAUST-6930V
dc.identifier.uri	http://hdl.handle.net/10754/670368
dc.description.sponsorship	This work was supported by the KAUST baseline funding.
dc.publisher	KAUST Research Repository
dc.rights	Attribution-NonCommercial 3.0 United States
dc.rights.uri	http://creativecommons.org/licenses/by-nc/3.0/us/
dc.subject	multispectral imaging
dc.subject	illumination spectra estimation
dc.subject	matrix factorization
dc.subject	ADMM
dc.subject	deep unrolling network
dc.title	Dataset for multispectral illumination estimation using deep unrolling network
dc.type	Dataset
dc.contributor.department	Visual Computing Center (VCC)
dc.contributor.institution	Ningbo University
dc.relation.issupplementto	Li, Y., Fu, Q., Heidrich, W. Multispectral illumination estimation using deep unrolling network. IEEE International Conference on Computer Vision (ICCV) 2021.
refterms.dateFOA	2021-08-17T00:00:00Z
kaust.request.doi	yes
display.summary	Our dataset repository includes 409 spectral reflectance images. The images consist of various indoor and outdoor scenes acquired with a compact scanning-based hyperspectral camera: Specim IQ. The indoor scenes include clothes, papers, toys, vegetables, fruits, optical elements, etc. and the outdoor scenes include buildings, plants, vehicles, animals, etc. The captured images have a spatial resolution of 512×512 pixels and 34 spectral bands ranging from 400nm to 730nm. <br><br> Details of the database can be found in the following publication: <br><br> @article{Yuqi2021SpecSeperation, <br>title={Multispectral illumination estimation using deep unrolling network}, <br>author={Li, Yuqi and Fu, Qiang and Heidrich, Wolfgang}, <br>booktitle={2021 IEEE International Conference on Computer Vision(ICCV)}, <br>pages={1--8}, <br>year={2021}, <br>organization={IEEE} } <br><br> We capture various scenes containing a whiteboard with flat spectral reflectance and calculate the reflectance spectral images. The spectral reflectance images are stored as H5 files. <br><br> Read the H5 data file (Matlab example): <br><br> data = h5read('*.h5','/img\');
display.relations	<b>Is Supplement To:</b> <br/><ul> <li><i>[Conference Paper]</i> <br/> Li, Y., Fu, Q., Heidrich, W. Multispectral illumination estimation using deep unrolling network. IEEE International Conference on Computer Vision (ICCV) 2021. Project Page: <a href="https://vccimaging.org/Publications/Yuqi2021SpecSeperation/">https://vccimaging.org/Publications/Yuqi2021SpecSeperation/</a></li></ul>