WaveletFormerNet：一种用于真实世界非均匀和浓雾去除的基于Transformer的小波网络
WaveletFormerNet: A Transformer-based Wavelet Network for Real-world Non-homogeneous and Dense Fog Removal
AI总结：
本文提出WaveletFormerNet，通过将离散小波变换嵌入Vision Transformer并引入并行卷积与特征聚合模块，有效缓解下采样导致的细节丢失与颜色失真，在真实非均匀和浓雾去除任务中优于现有方法且具备强泛化能力。
摘要：
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，但能够处理现实世界中复杂雾况（如浓雾或非均匀雾）下拍摄的图像至关重要。然而，现实世界中的雾分布十分复杂，随着特征图分辨率或图像分辨率的降低，下采样可能导致输出结果出现颜色失真或细节丢失。除了获取足够训练数据的挑战外，用于雾天图像处理的深度学习技术还可能出现过拟合，这会限制模型的泛化能力，对其在真实场景中的实际应用构成挑战。考虑到这些问题，本文提出了一种用于真实雾天图像恢复的基于Transformer的小波网络（WaveletFormerNet）。我们通过提出WaveletFormer和IWaveletFormer块，将离散小波变换嵌入到Vision 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块中引入了并行卷积，允许以轻量级机制捕获多频率信息。此外，我们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雾天图像恢复任务中的出色表现。大量实验表明，通过对较小模型复杂度的定量和定性评估，我们的WaveletFormerNet表现优于现有最先进方法。此外，我们在真实世界除尘和应用测试中取得的令人满意的结果，展示了WaveletFormerNet在计算机视觉相关应用中卓越的泛化能力和改进的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
