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Found 12 papers, 5 papers with code
Faithful Synthesis of Low-dose Contrast-enhanced Brain MRI Scans using Noise-preserving Conditional GANs
Our numerical experiments show that conditional GANs are suitable for generating images at different GBCA dose levels and can be used to augment datasets for virtual contrast models.
Learning Gradually Non-convex Image Priors Using Score Matching
In this paper, we propose a unified framework of denoising score-based models in the context of graduated non-convex energy minimization.
Explicit Diffusion of Gaussian Mixture Model Based Image Priors
In this work we tackle the problem of estimating the density $f_X$ of a random variable $X$ by successive smoothing, such that the smoothed random variable $Y$ fulfills $(\partial_t - \Delta_1)f_Y(\,\cdot\,, t) = 0$, $f_Y(\,\cdot\,, 0) = f_X$.
Constrained and unconstrained deep image prior optimization models with automatic regularization
Numerical results demonstrate the robustness with respect to image content, noise levels and hyperparameters of the proposed models on both denoising and deblurring of simulated as well as real natural and medical images.
Computed Tomography Reconstruction using Generative Energy-Based Priors
We apply the regularizer to limited-angle and few-view CT reconstruction problems, where it outperforms traditional reconstruction algorithms by a large margin.
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
To this end, we solve the linear inverse problem of undersampled MRI reconstruction in a variational setting.
Shared Prior Learning of Energy-Based Models for Image Reconstruction
We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data, which has three major building blocks: energy-based learning, a patch-based Wasserstein loss functional, and shared prior learning.
Accelerating Prostate Diffusion Weighted MRI using Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study
Compared to the reference images, the denoised images received higher image quality scores (p < 0. 0001).
Total Deep Variation: A Stable Regularizer for Inverse Problems
In this work, we combine the variational formulation of inverse problems with deep learning by introducing the data-driven general-purpose total deep variation regularizer.
Total Deep Variation for Linear Inverse Problems
Diverse inverse problems in imaging can be cast as variational problems composed of a task-specific data fidelity term and a regularization term.
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration
We investigate a well-known phenomenon of variational approaches in image processing, where typically the best image quality is achieved when the gradient flow process is stopped before converging to a stationary point.
Learning a Variational Network for Reconstruction of Accelerated MRI Data
Due to its high computational performance, i. e., reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow.
