  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.

Computer Vision and Pattern Recognition