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Communication Dans Un Congrès Année : 2020

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

Résumé

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.
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Dates et versions

hal-02902812 , version 1 (20-07-2020)

Identifiants

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Nelson Diaz, Camilo Noriega-Wandurraga, Adrian Basarab, Jean-Yves Tourneret, Henry Arguello. Adaptive Coded Aperture Design by Motion Estimation using Convolutional Sparse Coding in Compressive Spectral Video Sensing. 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Dec 2019, Le Gosier, Guadeloupe. pp.445-449, ⟨10.1109/CAMSAP45676.2019.9022649⟩. ⟨hal-02902812⟩
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