Haze usually leads to deteriorated images with low contrast, color shift and
structural distortion. We observe that many deep learning based models exhibit
exceptional performance on removing homogeneous haze, but they usually fail to
address the challenge of non-homogeneous dehazing. Two main factors account for
this situation. Firstly, due to the intricate and non uniform distribution of
dense haze, the recovery of structural and chromatic features with high
fidelity is challenging, particularly in regions with heavy haze. Secondly, the
existing small scale datasets for non-homogeneous dehazing are inadequate to
support reliable learning of feature mappings between hazy images and their
corresponding haze-free counterparts by convolutional neural network
(CNN)-based models. To tackle these two challenges, we propose a novel two
branch network that leverages 2D discrete wavelete transform (DWT), fast
Fourier convolution (FFC) residual block and a pretrained ConvNeXt model.
Specifically, in the DWT-FFC frequency branch, our model exploits DWT to
capture more high-frequency features. Moreover, by taking advantage of the
large receptive field provided by FFC residual blocks, our model is able to
effectively explore global contextual information and produce images with
better perceptual quality. In the prior knowledge branch, an ImageNet
pretrained ConvNeXt as opposed to Res2Net is adopted. This enables our model to
learn more supplementary information and acquire a stronger generalization
ability. The feasibility and effectiveness of the proposed method is
demonstrated via extensive experiments and ablation studies. The code is
available at this https URL

该研究提出了一种新的两分支网络，利用 2D 离散小波变换、快速傅里叶卷积残差块和预训练的 ConvNeXt 模型来解决雾霾图像非均质去雾的问题，并进行了大量实验验证其有效性。

突破雾霾：基于快速傅里叶卷积和 ConvNeXt 的先进非均匀去雾方法

Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method  based on Fast Fourier Convolution and ConvNeXt

Recently, there has been rapid and significant progress on image dehazing.
Many deep learning based methods have shown their superb performance in
handling homogeneous dehazing problems. However, we observe that even if a
carefully designed convolutional neural network (CNN) can perform well on
large-scaled dehazing benchmarks, the network usually fails on the
non-homogeneous dehazing datasets introduced by NTIRE challenges. The reasons
are mainly in two folds. Firstly, due to its non-homogeneous nature, the
non-uniformly distributed haze is harder to be removed than the homogeneous
haze. Secondly, the research challenge only provides limited data (there are
only 25 training pairs in NH-Haze 2021 dataset). Thus, learning the mapping
from the domain of hazy images to that of clear ones based on very limited data
is extremely hard. To this end, we propose a simple but effective approach for
non-homogeneous dehazing via ensemble learning. To be specific, we introduce a
two-branch neural network to separately deal with the aforementioned problems
and then map their distinct features by a learnable fusion tail. We show
extensive experimental results to illustrate the effectiveness of our proposed
method.

本文提出了一种简单而有效的经过集成学习的非均质去雾方法，该方法使用两个分支神经网络分别处理非均质去雾难题，并通过可学习的融合模块将它们的特征映射到一起，实现了在有限的数据情况下提高去雾效果的目的。