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Article Dans Une Revue IEEE Transactions on Image Processing Année : 2020

Compressive Color Pattern Detection using Partial Orthogonal Circulant Sensing Matrix

Résumé

One key issue in compressive sensing is to design a sensing matrix that is random enough to have a good signal reconstruction quality and that also enjoys some desirable properties such that orthogonality or being circulant. The classic method to construct such sensing matrices is to first generate a full orthogonal circulant matrix and then select only a few rows. In this paper, we propose a refined construction of orthogonal circulant sensing matrices that generates a circulant matrix where only a given subset of its rows are orthogonal. That way, the generation method is a lot less constrained leading to better sensing matrices and we still have the desired properties. The proposed partial shift-orthogonal sensing matrix is compared to random and learned sensing matrices in the frame of signal reconstruction. This sensing matrix is pattern-dependent and thus efficient to detect color patterns and edges from the measurements of a color image.
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Dates et versions

hal-02166497 , version 1 (11-12-2019)

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Sylvain Rousseau, David Helbert. Compressive Color Pattern Detection using Partial Orthogonal Circulant Sensing Matrix. IEEE Transactions on Image Processing, 2020, 29 (7), pp.670-678. ⟨10.1109/TIP.2019.2927334⟩. ⟨hal-02166497⟩
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