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Communication Dans Un Congrès Année : 2019

Cardiac Motion Estimation Using Convolutional Sparse Coding

Résumé

This paper studies a new motion estimation method based on convolutional sparse coding. The motion estimation problem is formulated as the minimization of a cost function composed of a data fidelity term, a spatial smoothness constraint, and a regularization based on convolution sparse coding. We study the potential interest of using a convolutional dictionary instead of a standard dictionary using specific examples. Moreover, the proposed method is evaluated in terms of motion estimation accuracy and compared with state-of-the-art algorithms, showing its interest for cardiac motion estimation.
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Dates et versions

hal-02887700 , version 1 (02-07-2020)

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Nelson Dias, Adrian Basarab, Jean-Yves Tourneret, Henry Arguello. Cardiac Motion Estimation Using Convolutional Sparse Coding. 27th European Signal Processing Conference (EUSIPCO 2019), Sep 2019, A Coruña, Spain. pp.1-5, ⟨10.23919/EUSIPCO.2019.8903163⟩. ⟨hal-02887700⟩
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