Diaz, Nelson and Noriega-Wandurraga, Camilo and Basarab, Adrian and Tourneret, Jean-Yves
and Arguello, Henry
Adaptive Coded Aperture Design by Motion Estimation using Convolutional Sparse Coding in Compressive Spectral Video Sensing.
(2020)
In: 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 15 December 2019 - 18 December 2019 (Le Gosier, Guadeloupe).
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(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 2MB |
Official URL: https://doi.org/10.1109/CAMSAP45676.2019.9022649
Abstract
This paper proposes a new motion estimation method based on convolutional sparse coding to adaptively design the colored-coded apertures in static and dynamic spectral videos. The motion in a spectral video is estimated from a low-resolution reconstruction of the datacube by training a convolutional dictionary per spectral band and solving a minimization problem. Simulations show improvements in terms of peak signal-to-noise ratio (of up to 2 dB) of the reconstructed videos by using the proposed approach, compared with state-of-art non-adaptive coded apertures.
Item Type: | Conference or Workshop Item (Paper) |
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Audience (conference): | International conference proceedings |
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Institution: | French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE) Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE) Other partners > Universidad Industrial de Santander - UIS (COLOMBIA) Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE) Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE) Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE) |
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Deposited On: | 20 Jul 2020 10:49 |
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