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Found 4 papers, 1 papers with code
Reconstructing classes of 3D FRI signals from sampled tomographic projections at unknown angles
By using the divergence theorem, we are able to retrieve the projected vertices of the polyhedron from the sampled tomographic projections, and then we show how to retrieve the 3D object and the projection angles from this information.
Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution
The translational equivariant nature of Convolutional Neural Networks (CNNs) is a reason for its great success in computer vision.
Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of Convolution Neural Network
In this study, inherent true noise information from 2-NEX acquisition was used to develop a deep-learning model based on residual learning of convolutional neural network (CNN), and this model was used to suppress the noise in 3D FSE MR images of knee joints.
LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging
Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts.
