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Convolutional sparse representations have recently attracted significant attention from the imaging community, thanks to their structural properties and their success in numerous imaging applications, ranging from restoration to super resolution and HDR imaging. Despite the development of efficient sparse coding and dictionary learning algorithms, several theoretical as well as practical aspects of these representations are still not thoroughly understood, and convolutional sparse representations remain, as a technique, far less mature than standard sparse representations. This minisymposium provides a selection of recent progress in this area, including dictionary learning algorithms, connections with deep learning, and new applications.