Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids

TL;DR

This paper introduces a multi-scale dehazing method combining Laplacian and Gaussian pyramids, addressing haze removal and noise amplification issues, and demonstrating superior performance over existing algorithms.

Contribution

It proposes a novel multi-scale dehazing algorithm using pyramids and a dark direct attenuation prior to improve haze removal and noise suppression.

Findings

Outperforms state-of-the-art dehazing algorithms

Prevents noise amplification in sky regions

Effectively preserves image details during dehazing

Abstract

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.