VSAnimator / stganLinks
☆44Updated 5 years ago
Alternatives and similar repositories for stgan
Users that are interested in stgan are comparing it to the libraries listed below
Sorting:
- Q. Zhang, Q. Yuan, J. Li, Z. Li, H. Shen, and L. Zhang, "Thick Cloud and Cloud Shadow Removal in Multitemporal Images using Progressively…☆71Updated 5 years ago
- ☆98Updated last year
- Cloud Removal for High-resolution Remote Sensing Imagery based on Generative Adversarial Networks.☆190Updated 2 years ago
- ☆70Updated 2 years ago
- Multimodal and Multiresolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark☆83Updated last year
- PyTorch Implementation of STGAN for Cloud Removal in Satellite Images.☆31Updated 5 years ago
- DSen2-CR: A network for removing clouds from Sentinel-2 images. This repo contains the model code, written in Python/Keras, as well as li…☆170Updated 2 weeks ago
- This is a cloud detection validation dataset for Sentinel-2A images☆15Updated 3 years ago
- GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion☆60Updated 3 years ago
- ☆19Updated 4 years ago
- ☆52Updated 2 years ago
- [Remote Sensing of Environment 2020] Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learnin…☆21Updated 2 years ago
- Open Satellite Image Cloud Detection Resources (Datasets and Tools)☆82Updated last year
- a dataset for remote sensing super-resolution☆47Updated 4 years ago
- [TGRS 2024] DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images☆67Updated 7 months ago
- This is an implementation of our CVPRW2017 paper "Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adv…☆26Updated 7 years ago
- Collection of popular and reproducible works of cloud detection and removal.☆30Updated 3 years ago
- A Flexible Spatiotemporal Fusion Model for Remote Sensing Images With Conditional Generative Adversarial Network☆50Updated 4 years ago
- ☆41Updated 4 years ago
- Codes for TGRS paper: DisOptNet: Distilling Semantic Knowledge from Optical Images for Weather-independent Building Segmentation