Also appears in collection : 2019 - T1 - WS3 - Imaging and machine learning

Recent advances in deep learning for tomographic reconstructions have shown a great promise to create accurate and high quality images from subsampled measurements in a time considerably shorter than needed by the established nonlinear regularisation methods such as e.g. TV. This new paradigm also offers a new implicit way of expressing prior knowledge through training on a class of images with expected characteristics. In this talk we discuss two common approaches to combining deep learning - here convolutional neural networks (CNN) - with model-based reconstruction techniques on the example of photoacoustic tomography. We also address particular challenges for learned reconstruction for such computationally intensive application.