Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids
This addresses image quality issues in dehazing for computer vision applications, but it is incremental as it builds on existing model-driven approaches.
The paper tackled the problems of ambiguity between object radiance and haze and noise amplification in sky regions for single image dehazing, proposing a multi-scale algorithm using Laplacian and Gaussian pyramids that outperforms state-of-the-art methods and prevents noise amplification.
Model driven single image dehazing was widely studied on top of different priors due to its extensive applications. Ambiguity between object radiance and haze and noise amplification in sky regions are two inherent problems of model driven single image dehazing. In this paper, a dark direct attenuation prior (DDAP) is proposed to address the former problem. A novel haze line averaging is proposed to reduce the morphological artifacts caused by the DDAP which enables a weighted guided image filter with a smaller radius to further reduce the morphological artifacts while preserve the fine structure in the image. A multi-scale dehazing algorithm is then proposed to address the latter problem by adopting Laplacian and Guassian pyramids to decompose the hazy image into different levels and applying different haze removal and noise reduction approaches to restore the scene radiance at different levels of the pyramid. The resultant pyramid is collapsed to restore a haze-free image. Experiment results demonstrate that the proposed algorithm outperforms state of the art dehazing algorithms and the noise is indeed prevented from being amplified in the sky region.