diegovalsesia/piunet
Permutation invariance and uncertainty in multitemporal image super-resolution
When analyzing satellite imagery, this tool helps improve the clarity of images captured at different times or from slightly different angles. It takes multiple low-resolution images of the same area and combines them to produce a single, sharper high-resolution image, along with an estimate of how confident the result is. This is ideal for remote sensing specialists, environmental scientists, or cartographers who rely on detailed aerial or satellite views.
No commits in the last 6 months.
Use this if you need to generate high-resolution images from multiple lower-quality, slightly misaligned satellite observations and understand the reliability of the enhanced image.
Not ideal if you only have a single low-resolution image, or if your primary goal is not satellite image enhancement.
Stars
35
Forks
7
Language
Jupyter Notebook
License
MIT
Category
Last pushed
May 12, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/diegovalsesia/piunet"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
HasnainRaz/Fast-SRGAN
A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
NatLabRockies/sup3r
The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial...
the-database/traiNNer-redux
Deep learning training framework for image super resolution and restoration.
eugenesiow/super-image
Image super resolution models for PyTorch.
movehand/raisr
A Python implementation of RAISR