liuh127/NTIRE-2021-Dehazing-DWGAN
Official PyTorch implementation of DW-GAN, 1st place solution of NTIRE 2021 NonHomogeneous Dehazing Challenge (CVPR Workshop 2021).
This tool helps improve the clarity of images taken in hazy conditions, especially when the haze is unevenly distributed across the scene. It takes hazy images as input and produces clearer, dehazed images, making details more visible. This is useful for anyone working with outdoor photography, surveillance, or remote sensing where atmospheric haze obscures visual information.
No commits in the last 6 months.
Use this if you need to automatically enhance the visibility of details in photographs or visual data affected by non-uniform atmospheric haze.
Not ideal if your images are affected by other visual impairments like blur, noise, or uniform fog rather than haze.
Stars
66
Forks
9
Language
Python
License
MIT
Category
Last pushed
May 30, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/liuh127/NTIRE-2021-Dehazing-DWGAN"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
VITA-Group/DeblurGANv2
[ICCV 2019] "DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better" by Orest Kupyn,...
KupynOrest/DeblurGAN
Image Deblurring using Generative Adversarial Networks
ZhendongWang6/Uformer
[CVPR 2022] Official implementation of the paper "Uformer: A General U-Shaped Transformer for...
shuochsu/DeepVideoDeblurring
S. Su, M. Delbracio, J. Wang, G. Sapiro, W. Heidrich, O. Wang. Deep Video Deblurring. CVPR 2017,...
advimman/HiDT
Official repository for the paper "High-Resolution Daytime Translation Without Domain Labels"...