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.

翻译：尽管深度卷积神经网络在去除合成雾方面取得了显著成功，但在实际应用中处理复杂雾天条件下（如浓雾或非均匀雾）拍摄的图像至关重要。然而，真实世界中的雾分布复杂，下采样会导致特征图或图像分辨率降低，进而使输出结果出现颜色失真或细节丢失。除了获取充足训练数据的挑战外，深度学习的雾天图像处理技术还可能出现过拟合问题，这会限制模型的泛化能力，为其在实际场景中的应用带来挑战。针对这些问题，本文提出了一种基于Transformer的小波网络（WaveletFormerNet）用于真实世界雾天图像恢复。我们通过提出WaveletFormer和IWaveletFormer模块，将离散小波变换嵌入视觉Transformer中，旨在缓解因下采样导致的图像纹理细节丢失和颜色失真。我们在Transformer模块中引入并行卷积，从而以轻量化机制捕获多频信息。此外，我们还实现了特征聚合模块（FAM），以保持图像分辨率并增强模型的特征提取能力，进一步提升了其在真实世界雾天图像恢复任务中的出色性能。大量实验表明，在模型复杂度较小的条件下，通过定量和定性评估，我们的WaveletFormerNet性能优于现有最先进方法。同时，在真实世界去尘和应用测试中的满意结果展示了WaveletFormerNet在计算机视觉相关应用中优越的泛化能力和改进的性能。