Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method
  based on Fast Fourier Convolution and ConvNeXt
Han Zhou Wei Dong Yangyi Liu Jun Chen
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 https://github.com/zhouh115/DWT-FFC.