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Found 9 papers, 8 papers with code
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless.
Image Reconstruction via Deep Image Prior Subspaces
Deep learning has been widely used for solving image reconstruction tasks but its deployability has been held back due to the shortage of high-quality training data.
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior
We investigate adaptive design based on a single sparse pilot scan for generating effective scanning strategies for computed tomography reconstruction.
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
Existing deep-learning based tomographic image reconstruction methods do not provide accurate estimates of reconstruction uncertainty, hindering their real-world deployment.
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.
Conditional Invertible Neural Networks for Medical Imaging
Over the last years, deep learning methods have become an increasingly popular choice to solve tasks from the field of inverse problems.
Blind Source Separation in Polyphonic Music Recordings Using Deep Neural Networks Trained via Policy Gradients
We propose a method for the blind separation of sounds of musical instruments in audio signals.
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods
In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime.
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
Deep Learning approaches for solving Inverse Problems in imaging have become very effective and are demonstrated to be quite competitive in the field.
