Progressive residual learning for single image dehazing
This work addresses image quality degradation in hazy conditions for computer vision applications, representing an incremental improvement by hybridizing existing methods.
The paper tackles the problem of single image dehazing by proposing a progressive residual learning strategy that combines physical model-free and model-based approaches, achieving state-of-the-art performance on public benchmarks with improved interpretability and adaptivity.
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.