no code implementations • 12 May 2020 • Gregory Ongie, Ajil Jalal, Christopher A. Metzler, Richard G. Baraniuk, Alexandros G. Dimakis, Rebecca Willett
Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging.
no code implementations • 30 Nov 2020 • Davis Gilton, Gregory Ongie, Rebecca Willett
Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging.
1 code implementation • 16 Feb 2021 • Davis Gilton, Gregory Ongie, Rebecca Willett
Recent efforts on solving inverse problems in imaging via deep neural networks use architectures inspired by a fixed number of iterations of an optimization method.
1 code implementation • 15 Feb 2024 • Megan Lantz, Emil Y. Sidky, Ingrid S. Reiser, Xiaochuan Pan, Gregory Ongie
Deep neural networks used for reconstructing sparse-view CT data are typically trained by minimizing a pixel-wise mean-squared error or similar loss function over a set of training images.