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.

翻译：然而,如果没有物理模型的指导,由于数据问题缺乏或不足,在实际情景中应用的性能会迅速降解;另一方面,物理模型方法具有更好的解释性,但因参数的多重目标优化而受到影响,这可能导致不尽人意的脱色结果;在本文件中,提议了一项渐进式留级学习战略,将物理模型脱色过程与重新组合的基于模型的脱色作业结合起来,这两种类型的脱色作业都具有脱色方法的优点;具体地说,全球大气光和传输图是互动优化的,同时提供了准确的残余信息和初步的无物理模型脱色过程的初步脱色恢复。拟议方法优于公共脱色基准的先进方法,更具有模型解释性和适应性,对复杂的污损数据更有利。