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Found 4 papers, 2 papers with code
Deep Equilibrium Architectures for Inverse Problems in Imaging
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.
Deep Learning Techniques for Inverse Problems in Imaging
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.
Model Adaptation for Inverse Problems in Imaging
Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging.
Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function
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.
