  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.
