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Found 12 papers, 1 papers with code
Low-Dose CT Image Reconstruction by Fine-Tuning a UNet Pretrained for Gaussian Denoising for the Downstream Task of Image Enhancement
The crucial point of our approach is that the neural network is pretrained on a distinctly different pretraining task with non-CT data, namely Gaussian noise removal on a variety of natural grayscale images (photographs).
Ranked #1 on
Medical Image Enhancement
on LoDoPaB-CT
Multi-Channel Potts-Based Reconstruction for Multi-Spectral Computed Tomography
We consider reconstructing multi-channel images from measurements performed by photon-counting and energy-discriminating detectors in the setting of multi-spectral X-ray computed tomography (CT).
Iterative Potts minimization for the recovery of signals with discontinuities from indirect measurements -- the multivariate case
A frequent task is to find the segments of the signal or image which corresponds to finding the discontinuities or jumps in the data.
Wavelet Sparse Regularization for Manifold-Valued Data
In this paper, we consider the sparse regularization of manifold-valued data with respect to an interpolatory wavelet/multiscale transform.
Variational Regularization of Inverse Problems for Manifold-Valued Data
In this paper, we consider the variational regularization of manifold-valued data in the inverse problems setting.
Fast Piecewise-Affine Motion Estimation Without Segmentation
Current algorithmic approaches for piecewise affine motion estimation are based on alternating motion segmentation and estimation.
Learning Steerable Filters for Rotation Equivariant CNNs
In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input.
Ranked #2 on
Breast Tumour Classification
on PCam
Breast Tumour Classification
Colorectal Gland Segmentation:
+2
Model-based learning of local image features for unsupervised texture segmentation
We note that the features can be learned from a small set of images, from a single image, or even from image patches.
Total Variation Regularization of Shape Signals
This paper introduces the concept of shape signals, i. e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals.
Mumford-Shah and Potts Regularization for Manifold-Valued Data with Applications to DTI and Q-Ball Imaging
For the multivariate Mumford-Shah and Potts problems (for image regularization) we propose a splitting into suitable subproblems which we can solve exactly using the techniques developed for the corresponding univariate problems.
Fast Partitioning of Vector-valued Images
We propose a fast splitting approach to the classical variational formulation of the image partitioning problem, which is frequently referred to as the Potts or piecewise constant Mumford--Shah model.
Total variation regularization for manifold-valued data
For the class of Cartan-Hadamard manifolds (which includes the data space in diffusion tensor imaging) we show the convergence of the proposed TV minimizing algorithms to a global minimizer.
