Collection Imaging and machine learning
Organizer(s)
Date(s)
03/05/2024
Learned image reconstruction for high-resolution tomographic imaging
By Marta Betcke
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.
