62988 20190709135427.0 doi 10.1111/cgf.13086 sideral 99252 ART-2017-99252 eng (orcid)0000-0002-7796-3177 Serrano, Ana Universidad de Zaragoza Convolutional Sparse Coding for Capturing High-Speed Video Content 2017 Access copy available to the general public Unrestricted Video capture is limited by the trade-off between spatial and temporal resolution: when capturing videos of high temporal resolution, the spatial resolution decreases due to bandwidth limitations in the capture system. Achieving both high spatial and temporal resolution is only possible with highly specialized and very expensive hardware, and even then the same basic trade-off remains. The recent introduction of compressive sensing and sparse reconstruction techniques allows for the capture of single-shot high-speed video, by coding the temporal information in a single frame, and then reconstructing the full video sequence from this single-coded image and a trained dictionary of image patches. In this paper, we first analyse this approach, and find insights that help improve the quality of the reconstructed videos. We then introduce a novel technique, based on convolutional sparse coding (CSC), and show how it outperforms the state-of-the-art, patch-based approach in terms of flexibility and efficiency, due to the convolutional nature of its filter banks. The key idea for CSC high-speed video acquisition is extending the basic formulation by imposing an additional constraint in the temporal dimension, which enforces sparsity of the first-order derivatives over time. info:eu-repo/grantAgreement/ES/MINECO/IMAGER-TIN2016-79710-P info:eu-repo/grantAgreement/ES/MINECO/BLINK 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/grantAgreement/ES/MINECO/Lightslice info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 2.046 2017 COMPUTER SCIENCE, SOFTWARE ENGINEERING 22 / 104 = 0.212 2017 Q1 T1 0.597 2017 Computer Networks and Communications 2017 Q1 Computer Graphics and Computer-Aided Design 2017 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion (orcid)0000-0003-3509-8485 Garces, Elena (orcid)0000-0003-0060-7278 Masia, Belen Universidad de Zaragoza (orcid)0000-0002-7503-7022 Gutierrez, Diego Universidad de Zaragoza 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 36, 8 (2017), 380-389 Comput. graph. forum COMPUTER GRAPHICS FORUM 0167-7055 5705159 http://zaguan.unizar.es/record/62988/files/texto_completo.pdf Versión publicada 93707 http://zaguan.unizar.es/record/62988/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:62988 articulos driver 2019-07-09-11:29:54 ARTICLE