This is the official PyTorch implementation of KA_Net.

Restoring high-quality images from degraded hazy observations is a fundamental and essential task in the field of computer vision. While deep models have achieved significant success with synthetic data, their effectiveness in real-world scenarios remains uncertain. To improve adaptability in real-world environments, we construct an entirely new computational framework by making efforts from three key aspects: imaging perspective, structural modules, and training strategies. To simulate the often-overlooked multiple degradation attributes found in real-world hazy images, we develop a new hazy imaging model that encapsulates multiple degraded factors, assisting in bridging the domain gap between synthetic and real-world image spaces. In contrast to existing approaches that primarily address the inverse imaging process, we design a new dehazing network following the “localization-and-removal” pipeline. The degradation localization module aims to assist in network capture discriminative haze-related feature information, and the degradation removal module focuses on eliminating dependencies between features by learning a weighting matrix of training samples, thereby avoiding spurious correlations of extracted features in existing deep methods. We also define a new Gaussian perceptual contrastive loss to further constrain the network to update in the direction of the natural dehazing. Regarding multiple full/no-reference image quality indicators and subjective visual effects on challenging RTTS, URHI, and Fattal real hazy datasets, the proposed method has superior performance and is better than the current state-of-the-art methods.

See more details in [paper]

• Windows: 10

• CUDA Version: 11.0

• Python 3.7

• torch==1.7.0
• torchvision==0.7.0
• NVIDIA GPU and CUDA

1. Download Dehaze weights and Extraction code: [n2k4]
2. We release part of the data used for training, please download them if you need.( https://pan.baidu.com/s/1kaKo06PGAHjEQIMfNAgbkQ?pwd=sw6s) and Extraction code:[sw6s]
3. (**Note that the final complete version of the data and synthetic code will be released soon.)

Our test run is simple, just change the input and output paths according to your requirements

python KA_net_test.py

Please note that due to time, our training code is not fully sorted out yet, but it won't take long, so stay tuned

Look at the output in the output folder

We thank the authors of Transweather. Part of our code is built upon their modules.

If our work helps your research, please consider to cite our paper:

Y. Feng, L. Ma, X. Meng, F. Zhou, R. Liu and Z. Su, "Advancing Real-World Image Dehazing: Perspective, Modules, and Training," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2024.3416731