75680 20190819101350.0 doi 10.1145/3130800.3130810 sideral 104309 ART-2017-104309 eng Choi, I. High-quality hyperspectral reconstruction using a spectral prior 2017 Access copy available to the general public Unrestricted We present a novel hyperspectral image reconstruction algorithm, which overcomes the long-standing tradeoff between spectral accuracy and spatial resolution in existing compressive imaging approaches. Our method consists of two steps: First, we learn nonlinear spectral representations from real-world hyperspectral datasets; for this, we build a convolutional autoencoder, which allows reconstructing its own input through its encoder and decoder networks. Second, we introduce a novel optimization method, which jointly regularizes the fidelity of the learned nonlinear spectral representations and the sparsity of gradients in the spatial domain, by means of our new fidelity prior. Our technique can be applied to any existing compressive imaging architecture, and has been thoroughly tested both in simulation, and by building a prototype hyperspectral imaging system. It outperforms the state-of-the-art methods from each architecture, both in terms of spectral accuracy and spatial resolution, while its computational complexity is reduced by two orders of magnitude with respect to sparse coding techniques. Moreover, we present two additional applications of our method: hyperspectral interpolation and demosaicing. Last, we have created a new high-resolution hyperspectral dataset containing sharper images of more spectral variety than existing ones, available through our project website. info:eu-repo/grantAgreement/ES/MINECO/TIN2016-78753-P This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 682080-CHAMELEON info:eu-repo/grantAgreement/EC/H2020/682080/EU/Intuitive editing of visual appearance from real-world datasets/CHAMELEON info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 4.384 2017 COMPUTER SCIENCE, SOFTWARE ENGINEERING 3 / 104 = 0.029 2017 Q1 T1 1.344 2017 Computer Graphics and Computer-Aided Design 2017 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Jeon, D.S. Nam, G. (orcid)0000-0002-7503-7022 Gutierrez, D. Universidad de Zaragoza Kim, M.H. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 36, 6 (2017), 218 [13 pp] ACM trans. graph. ACM TRANSACTIONS ON GRAPHICS 0730-0301 1814607 http://zaguan.unizar.es/record/75680/files/texto_completo.pdf Postprint 103408 http://zaguan.unizar.es/record/75680/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:75680 articulos driver 2019-08-19-09:50:51 ARTICLE