Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms.
Existing reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution, which may fail to provide sufficient information about the ever-changing environment with high complexity.
In this work, we present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the human body.
The anisotropic volume's high-resolution (HR) plane is used to build the HR-LR image pairs for model training.
The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.
NeRF-based SVCT methods represent the desired CT image as a continuous function of spatial coordinates and train a Multi-Layer Perceptron (MLP) to learn the function by minimizing loss on the SV sinogram.
This function was parameterized by a neural network and learned directly from the measured k-space itself without additional fully sampled high-quality training data.
Using implicit neural representation, we construct a continuous and noise-free longitudinal fetus brain atlas as a function of the 4D spatial-temporal coordinate.
Experimental results of simulated noise and real microscopy noise removal show that Noise2SR outperforms two blind-spot based self-supervised deep learning image denoising methods.
Compared with recent related works that solve similar problems using implicit neural representation network (INR), our essential contribution is an effective and simple re-projection strategy that pushes the tomography image reconstruction quality over supervised deep learning CT reconstruction works.
In the ArSSR model, the reconstruction of HR images with different up-scaling rates is defined as learning a continuous implicit voxel function from the observed LR images.
For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction.
Modern deep neural networks rely on overparameterization to achieve state-of-the-art generalization.
Specifically, in terms of BLEU-4 and Word Error Rate (WER), our performance has reached 94. 50% and 2. 65% on the redundant test set; 92. 30% and 3. 48% on the purified test set.
In this study, we model such local nonlinearity of dendritic trees with our dendritic neural network (DENN) structure and apply this structure to typical machine learning tasks.
In this paper, we propose a deep learning based reconstruction of 3D human body shape from 2D orthographic views.
1 code implementation • 30 May 2018 • Can Li, Zhongrui Wang, Mingyi Rao, Daniel Belkin, Wenhao Song, Hao Jiang, Peng Yan, Yunning Li, Peng Lin, Miao Hu, Ning Ge, John Paul Strachan, Mark Barnell, Qing Wu, R. Stanley Williams, J. Joshua Yang, Qiangfei Xia
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence.
Emerging Technologies Applied Physics
Our experiments show that different adversarial strengths, i. e., perturbation levels of adversarial examples, have different working zones to resist the attack.
IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and achieves ultrahigh execution parallelism and power efficiency.