In the domain of signal and image processing, finding and using appropriate representations for the data being analysed is a critical step in extracting valuable information. Recent advances in methodology and optimisation investigate the use of sophisticated learning and modelling strategies to preserve the intrinsic properties of physical signals that typically live in low-dimensional non-linear manifolds. Potential applications include astronomy, astrophysics, medicine and computer vision.
The purpose of this international workshop is to discuss cutting-edge ideas and explore new strategies to model and restore signals living in these known or unknown complicated spaces. Topics include dictionary learning on manifolds, deep learning, optimisation and inverse problems on manifolds.