  The recent physical model-free dehazing methods have achieved
state-of-the-art performances. However, without the guidance of physical
models, the performances degrade rapidly when applied to real scenarios due to
the unavailable or insufficient data problems. On the other hand, the physical
model-based methods have better interpretability but suffer from
multi-objective optimizations of parameters, which may lead to sub-optimal
dehazing results. In this paper, a progressive residual learning strategy has
been proposed to combine the physical model-free dehazing process with
reformulated scattering model-based dehazing operations, which enjoys the
merits of dehazing methods in both categories. Specifically, the global
atmosphere light and transmission maps are interactively optimized with the aid
of accurate residual information and preliminary dehazed restorations from the
initial physical model-free dehazing process. The proposed method performs
favorably against the state-of-the-art methods on public dehazing benchmarks
with better model interpretability and adaptivity for complex hazy data.
