neumann_networks_code
Inverse imaging model
An implementation of Neumann Networks for Inverse Problems in Imaging using Python and TensorFlow.
Neumann Networks for Inverse Problems in Imaging
14 stars
2 watching
4 forks
Language: Python
last commit: over 5 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
 ethanhe42/u-net | A convolutional neural network architecture for biomedical image segmentation | 430 |
 adalca/neurite | A toolbox for building neural networks that analyze medical images | 340 |
 homles11/igcv3 | An implementation of an efficient deep neural network architecture | 189 |
 biomedical-imaging-group/globalbioim | A Matlab framework for solving inverse problems in computational imaging | 80 |
 dltk/dltk | A toolkit for building and prototyping deep learning models for medical image analysis | 1,431 |
 ysnan/nbd_kerunc | A repository providing pre-trained models and results for image deconvolution in the presence of kernel/model uncertainty | 14 |
 fabianbormann/tensorflow-deconvnet-segmentation | An implementation of a deep learning algorithm for image segmentation using convolutional neural networks | 220 |
 neroloh/darts-tensorflow | This implementation provides a Tensorflow-based framework for searching different architectures in deep neural networks using 2nd-order approximations. | 73 |
 honlan/dmt | This project enables makeup transfer using a Generative Adversarial Network to disentangle facial identity and makeup features. | 118 |
 chenxi116/pnasnet.tf | An implementation of PNASNet-5 architecture in TensorFlow for image classification on ImageNet. | 102 |
 zhengpeng7/birefnet | An open-source implementation of an image segmentation model that combines background removal and object detection capabilities. | 1,484 |
 marvinteichmann/tensorflow-fcn | An implementation of a fully convolutional network architecture for image segmentation using VGG weights. | 1,102 |
 nv-tlabs/gscnn | This code implements a neural network architecture designed to perform semantic segmentation in computer vision tasks. | 920 |
 datake/adagcn | An implementation of an AdaGCN model that adapts Graph Convolutional Networks to deep models for graph neural networks tasks. | 60 |
 preritj/segmentation | Deep learning models for semantic segmentation of images | 101 |