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Found 10 papers, 6 papers with code
X-ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case Study
We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system.
Multi-stage Deep Learning Artifact Reduction for Computed Tomography
As an alternative, we propose a multi-stage deep learning method for artifact removal, in which neural networks are applied to several domains, similar to a classical CT processing pipeline.
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning
We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks.
Quantifying the effect of X-ray scattering for data generation in real-time defect detection
X-ray scattering is known to be computationally expensive to simulate, and this effect can heavily influence the accuracy of a generated X-ray image.
CoShaRP: A Convex Program for Single-shot Tomographic Shape Sensing
We introduce single-shot X-ray tomography that aims to estimate the target image from a single cone-beam projection measurement.
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains
Neural network pruning techniques can substantially reduce the computational cost of applying convolutional neural networks (CNNs).
A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks
Moreover, we show that the training time of an NN-FDK network is orders of magnitude lower than the considered deep neural networks, with only a slight reduction in reconstruction accuracy.
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography
To overcome this issue, we propose Noise2Filter, a learned filter method that can be trained using only the measured data, and does not require any additional training data.
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.
Noise2Inverse: Self-supervised deep convolutional denoising for tomography
Recovering a high-quality image from noisy indirect measurements is an important problem with many applications.
