- Convolutional neural networks🔍
- Self|Supervised Deep Learning for Image Reconstruction🔍
- Structure|preserving low|dose computed tomography image ...🔍
- CT image denoising methods for image quality improvement and ...🔍
- Toward denoising of 3D CT scans with few data🔍
- On instabilities of deep learning in image reconstruction and ...🔍
- Self|supervised Denoising for Dynamic Medical Imaging🔍
- Super resolution|based methodology for self|supervised ...🔍
Self|supervised deep image restoration for x|ray computed ...
Convolutional neural networks: an overview and application in ...
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer ...
Self-Supervised Deep Learning for Image Reconstruction: A ...
Low dose X-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops image reconstruction methods with deep learning (DL) ...
Structure-preserving low-dose computed tomography image ...
Minimizing X-ray exposure to patients has been one of the major efforts undertaken in the CT field (2,3). As the tube current [milliampere-seconds (mAs)] is ...
CT image denoising methods for image quality improvement and ...
... X-ray CT image processing and reconstruction: a review. Med Phys ... A review on deep learning approaches for low-dose computed tomography ...
Toward denoising of 3D CT scans with few data - Imec-Vision Lab
DPIR (Deep Plug-and-Play Image Restoration network) explores a way to combine both supervised deep learning and model- based denoising methods by first ...
On instabilities of deep learning in image reconstruction and ... - PNAS
These techniques form the foundation for essential tools across the physical and life sciences such as MRI, computerized tomography (CT), ...
Self-supervised Denoising for Dynamic Medical Imaging
With the success of neural net- works in computer vision, supervised deep learning methods show promi- nent performance in single-image ...
Super resolution-based methodology for self-supervised ... - Frontiers
Dong, C., Loy, C. C., He, K., and Tang, X. (2014). “Learning a deep convolutional network for image super-resolution,” in Computer Vision ...
Enabling Low-Dose In Vivo Benchtop X-ray Fluorescence ...
... deep feature extraction and high-quality image reconstruction. We ... X-ray fluorescence computed tomography (XFCT) image reconstruction.
Image Restoration with Mean-Reverting Stochastic Differential ...
... Computer Vision and Pattern Recognition (CVPR), pp. 2737–2746, 2020. Zhang, L. and Zuo, W. Image restoration: from sparse and low-rank priors to deep priors.
Self-Supervised Deep Learning for Image Reconstruction: A ...
The proposed method is applied to solve linear and nonlinear inverse problems, specifically, sparse-view computed tomography image reconstruction and phase ...
Image Quality and Lesion Detection on Deep Learning ...
Fast model-based X-ray CT reconstruction using spatially ... Convolutional neural network based metal artifact reduction in x-ray computed tomography.
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT ...
In this work, we propose a unified supervised-unsupervised (SUPER) learning framework for X-ray computed tomography (CT) image reconstruction. The proposed ...
Shot noise reduction in radiographic and tomographic multi-channel ...
... imaging with self-supervised deep learning ... Cernik, “3d chemical imaging in the laboratory by hyperspectral x-ray computed tomography,” Sci.
Learning Enriched Features for Real Image Restoration and ...
image super-resolution. In: ECCV (2014) 3. 27. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convo- lutional networks. TPAMI (2015) ...
Full Image-Index Remainder Based Single Low-Dose DR/CT Self ...
Huang T, Li S, Jia X, Lu H, and Liu J Neighbor2Neighbor: a self-supervised framework for deep image denoising IEEE Trans. Image Process.
Self-supervised deep denoising for synchrotron tomography
A deep convolutional neural network using directional wavelets for low-dose. X-ray CT reconstruction. Medical Physics, 2017. Page 31. Deep ...
High-fidelity Image Restoration of Large 3D Electron Microscopy ...
Such image artifacts complicate further processing both for automated computer vision methods and human experts. Unfortunately, the widely used ...
Deep learning tomographic reconstruction through hierarchical ...
Paper presented at the 6th International Conference on Image Formation in X-Ray Computed Tomography, Regensburg, 3–7 August 2020. Google ...
Improving throughput and image quality of high-resolution 3D X-ray ...
It is now well known, however, that deep learning (DL) based algorithms for CT reconstruction can improve the scan time (throughput) and image quality ...