DeblurGanToDehaze

[ICCV 2019] "DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better" by Orest Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang

Tensorflow implementation of DeblurGAN(Blind Motion Deblurring Using Conditional Adversarial Networks)

An easy-to-read implementation of DeblurGAN

This is a lightweight GAN developed for real-time deblurring. The model has a super tiny size and a rapid inference time. The motivation is to boost marker detection in robotic applications, however, you may use it for other applications definitely.

Dataset and code of our AAAI2022 paper "Transmission-Guided Bayesian Generative Model for Smoke Segmentation"

Analytics DSL for Python

Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-world Hazy Scenes; accepted by Sensors?2021,?21(3), 960, MDPI; https://doi.org/10.3390/s21030960

This is a very simplified ipynb code for KupynOrest's Deblur GAN code. DeblurGAN addresses the challenge of end-to-end image deblurring through the use of conditional Generative Adversarial Networks (cGANs).I have used pytorch for this implementation.