Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenges. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing approach that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation study to demonstrate the effectiveness of our proposed method.

翻译：近年来，图像去雾问题受到越来越多的关注。许多深度学习方法被提出以应对这一挑战，并在处理均匀雾霾方面取得了显著进展。然而，当这些方法应用于非均匀雾霾图像（如NTIRE挑战赛引入的NH-HAZE23数据集）时，无法保持同等性能。此类失败的原因之一在于，非均匀雾霾并不符合均匀雾霾建模所需的基本假设。此外，传统端到端训练方法需要大量非均匀雾霾图像与清晰图像的配对数据，而NH-HAZE23数据集的规模有限。尽管可以通过利用其他非均匀去雾数据集来扩充NH-HAZE23数据集，但我们发现，必须设计适当的数据预处理方法以减少目标数据集与扩充数据集之间的分布差异。这一发现实际上与数据中心人工智能的理念相一致。通过新颖的网络架构和一种能够系统性提升数据质量的数据预处理方法，我们提出了一种创新的去雾方法。具体而言，我们对扩充后的数据集应用逐RGB通道的变换，并在双分支框架中引入当前最先进的Transformer作为主干网络。我们通过大量实验和消融研究证明了所提方法的有效性。