Existing dehazing approaches struggle to process real-world hazy images owing
to the lack of paired real data and robust priors. In this work, we present a
new paradigm for real image dehazing from the perspectives of synthesizing more
realistic hazy data and introducing more robust priors into the network.
Specifically, (1) instead of adopting the de facto physical scattering model,
we rethink the degradation of real hazy images and propose a phenomenological
pipeline considering diverse degradation types. (2) We propose a Real Image
Dehazing network via high-quality Codebook Priors (RIDCP). Firstly, a VQGAN is
pre-trained on a large-scale high-quality dataset to obtain the discrete
codebook, encapsulating high-quality priors (HQPs). After replacing the
negative effects brought by haze with HQPs, the decoder equipped with a novel
normalized feature alignment module can effectively utilize high-quality
features and produce clean results. However, although our degradation pipeline
drastically mitigates the domain gap between synthetic and real data, it is
still intractable to avoid it, which challenges HQPs matching in the wild.
Thus, we re-calculate the distance when matching the features to the HQPs by a
controllable matching operation, which facilitates finding better counterparts.
We provide a recommendation to control the matching based on an explainable
solution. Users can also flexibly adjust the enhancement degree as per their
preference. Extensive experiments verify the effectiveness of our data
synthesis pipeline and the superior performance of RIDCP in real image
dehazing.

本文提出了一种基于深度学习的新范式，通过合成更真实的数据和引入更强的先验知识来处理真实世界中的雾霾图像。所提出的 Real Image Dehazing 网络（RIDCP）结合高质量的 Codebook Priors 来处理多种复杂的视觉退化现象，其效果优于传统物理散射模型的处理方式，并能显著提高图像的清晰度。经过大量实验证明了这种模型对合成数据和真实数据的处理能力。