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.