Although deep convolutional neural networks have achieved remarkable success
in removing synthetic fog, it is essential to be able to process images taken
in complex foggy conditions, such as dense or non-homogeneous fog, in the real
world. However, the haze distribution in the real world is complex, and
downsampling can lead to color distortion or loss of detail in the output
results as the resolution of a feature map or image resolution decreases. In
addition to the challenges of obtaining sufficient training data, overfitting
can also arise in deep learning techniques for foggy image processing, which
can limit the generalization abilities of the model, posing challenges for its
practical applications in real-world scenarios. Considering these issues, this
paper proposes a Transformer-based wavelet network (WaveletFormerNet) for
real-world foggy image recovery. We embed the discrete wavelet transform into
the Vision Transformer by proposing the WaveletFormer and IWaveletFormer
blocks, aiming to alleviate texture detail loss and color distortion in the
image due to downsampling. We introduce parallel convolution in the Transformer
block, which allows for the capture of multi-frequency information in a
lightweight mechanism. Additionally, we have implemented a feature aggregation
module (FAM) to maintain image resolution and enhance the feature extraction
capacity of our model, further contributing to its impressive performance in
real-world foggy image recovery tasks. Extensive experiments demonstrate that
our WaveletFormerNet performs better than state-of-the-art methods, as shown
through quantitative and qualitative evaluations of minor model complexity.
Additionally, our satisfactory results on real-world dust removal and
application tests showcase the superior generalization ability and improved
performance of WaveletFormerNet in computer vision-related applications.

本文提出了一种基于 Transformer 和小波的网络 (WaveletFormerNet) 用于现实世界中的雾图像恢复，它通过将离散小波变换嵌入到 Vision Transformer 中来缓解由于降采样导致的图像质量损失和颜色失真，同时引入了并行卷积和特征聚合模块以提高模型的性能和泛化能力。实验结果表明，WaveletFormerNet 在雾图像恢复和计算机视觉应用中具有比现有方法更好的效果。