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Deep Microlocal Reconstruction for Limited-Angle Tomography by Andrade-Loarca, Héctor; Kutyniok, Gitta; Öktem, Ozan; Petersen, Philipp is a scholarly article available to read on EtoBox.

What is Deep Microlocal Reconstruction for Limited-Angle Tomography about?

We present a deep learning-based algorithm to jointly solve a reconstruction problem and a wavefront set extraction problem in tomographic imaging. The algorithm is based on a recently developed digital wavefront set extractor as well as the well-known microlocal canonical relation for the Radon transform. We use the wavefront set information about x-ray data to improve the reconstruction by requiring that the underlying neural networks simultaneously extract the correct ground truth wavefront set and ground truth image. As a necessary theoretical step, we identify the digital microlocal canonical relations for deep convolutional residual neural networks. We find strong numerical evidence for the effectiveness of this approach.

Author

Andrade-Loarca, Héctor; Kutyniok, Gitta; Öktem, Ozan; Petersen, Philipp

Published

2021

Language

EN