Rsdn
Code for paper "Video Super-Resolution with Recurrent Structure-Detail Network" . The code is under preparation and is coming soon.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Image Super Resolution | 4,392 | 4 months ago | 106 | apache-2.0 | Python | |||||
| 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks. | ||||||||||
| Awesome Gan For Medical Imaging | 1,919 | 2 years ago | 2 | |||||||
| Awesome GAN for Medical Imaging | ||||||||||
| Super Resolution.benckmark | 735 | 4 years ago | ||||||||
| Benchmark and resources for single super-resolution algorithms | ||||||||||
| Dcscn Super Resolution | 687 | a year ago | 37 | mit | Python | |||||
| A tensorflow implementation of "Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network", a deep learning based Single-Image Super-Resolution (SISR) model. | ||||||||||
| Caffe Vdsr | 264 | 5 years ago | 6 | mit | Matlab | |||||
| A Caffe-based implementation of very deep convolution network for image super-resolution | ||||||||||
| Ignn | 201 | 3 years ago | 2 | Python | ||||||
| Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20) | ||||||||||
| Master Thesis Bayesiancnn | 197 | 5 years ago | mit | TeX | ||||||
| Master Thesis on Bayesian Convolutional Neural Network using Variational Inference | ||||||||||
| Deep_learning_tutorial | 192 | 7 years ago | Jupyter Notebook | |||||||
| [ko] 패스트캠퍼스 강의 자료 | ||||||||||
| Awesome Super Resolution | 149 | 5 years ago | HTML | |||||||
| A curated list of awesome super-resolution resources. | ||||||||||
| Fast Style Transfer | 148 | 7 years ago | 7 | gpl-3.0 | Python | |||||
| A TensorFlow implementation of real-time style transfer based on the paper 'Perceptual Losses for Real-Time Style Transfer and Super-Resolution' by Johnson et. al | ||||||||||
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