000075680 001__ 75680 000075680 005__ 20190819101350.0 000075680 0247_ $$2doi$$a10.1145/3130800.3130810 000075680 0248_ $$2sideral$$a104309 000075680 037__ $$aART-2017-104309 000075680 041__ $$aeng 000075680 100__ $$aChoi, I. 000075680 245__ $$aHigh-quality hyperspectral reconstruction using a spectral prior 000075680 260__ $$c2017 000075680 5060_ $$aAccess copy available to the general public$$fUnrestricted 000075680 5203_ $$aWe 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. 000075680 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TIN2016-78753-P$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 682080-CHAMELEON$$9info:eu-repo/grantAgreement/EC/H2020/682080/EU/Intuitive editing of visual appearance from real-world datasets/CHAMELEON 000075680 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000075680 590__ $$a4.384$$b2017 000075680 591__ $$aCOMPUTER SCIENCE, SOFTWARE ENGINEERING$$b3 / 104 = 0.029$$c2017$$dQ1$$eT1 000075680 592__ $$a1.344$$b2017 000075680 593__ $$aComputer Graphics and Computer-Aided Design$$c2017$$dQ1 000075680 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000075680 700__ $$aJeon, D.S. 000075680 700__ $$aNam, G. 000075680 700__ $$0(orcid)0000-0002-7503-7022$$aGutierrez, D.$$uUniversidad de Zaragoza 000075680 700__ $$aKim, M.H. 000075680 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000075680 773__ $$g36, 6 (2017), 218 [13 pp]$$pACM trans. graph.$$tACM TRANSACTIONS ON GRAPHICS$$x0730-0301 000075680 8564_ $$s1814607$$uhttps://zaguan.unizar.es/record/75680/files/texto_completo.pdf$$yPostprint 000075680 8564_ $$s103408$$uhttps://zaguan.unizar.es/record/75680/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000075680 909CO $$ooai:zaguan.unizar.es:75680$$particulos$$pdriver 000075680 951__ $$a2019-08-19-09:50:51 000075680 980__ $$aARTICLE