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Adaptive Coded Aperture Design by Motion Estimation using Convolutional Sparse Coding in Compressive Spectral Video Sensing

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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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)
Audience (conference):International conference proceedings
Uncontrolled Keywords:
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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