fourson/Learning-to-dehaze-with-polarization
NeurIPS 2021 paper: Learning to Dehaze with Polarization
This project helps improve the clarity of images captured in hazy conditions, a common issue caused by atmospheric scattering that reduces visibility. It takes specialized polarization camera images as input and outputs a significantly clearer, dehazed version of the scene. This is ideal for professionals in fields like surveillance, autonomous vehicle development, or remote sensing who need to analyze visual data captured in adverse weather.
No commits in the last 6 months.
Use this if you need to automatically enhance the visibility and detail of images taken through haze, especially those captured with polarization-aware cameras.
Not ideal if you only have standard, single-shot RGB images and do not have access to polarization camera data.
Stars
41
Forks
3
Language
Python
License
—
Category
Last pushed
May 18, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/fourson/Learning-to-dehaze-with-polarization"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
cszn/KAIR
Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet,...
gabrieleilertsen/hdrcnn
HDR image reconstruction from a single exposure using deep CNNs
INVOKERer/DeepRFT
The code for 'Intriguing Findings of Frequency Selection for Image Deblurring' and 'Deep...
emidan19/deep-tempest
Restoration for TEMPEST images using deep-learning
VinAIResearch/blur-kernel-space-exploring
Exploring Image Deblurring via Blur Kernel Space (CVPR'21)