A review on dark channel prior based image dehazing algorithms

Sungmin Lee,Chee Sun Won,Ju-Hun Nam,Seokmin Yun,Seung-Won Jung

doi:10.1186/s13640-016-0104-y

Sungmin Lee, Chee Sun Won + Show 3 more

Open Access

https://doi.org/10.1186/s13640-016-0104-y

Copy DOI

Abstract

The presence of haze in the atmosphere degrades the quality of images captured by visible camera sensors. The removal of haze, called dehazing, is typically performed under the physical degradation model, which necessitates a solution of an ill-posed inverse problem. To relieve the difficulty of the inverse problem, a novel prior called dark channel prior (DCP) was recently proposed and has received a great deal of attention. The DCP is derived from the characteristic of natural outdoor images that the intensity value of at least one color channel within a local window is close to zero. Based on the DCP, the dehazing is accomplished through four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. This four-step dehazing process makes it possible to provide a step-by-step approach to the complex solution of the ill-posed inverse problem. This also enables us to shed light on the systematic contributions of recent researches related to the DCP for each step of the dehazing process. Our detailed survey and experimental analysis on DCP-based methods will help readers understand the effectiveness of the individual step of the dehazing process and will facilitate development of advanced dehazing algorithms.

Highlights

Due to absorption and scattering by atmospheric particles in haze, outdoor images have poor visibility under inclement weather
1.2.2 Dark channel prior (DCP) He et al [10] performed an empirical investigation of the characteristic of haze-free outdoor images
1.3 Analysis of dark channel prior (DCP)-based dehazing algorithms In Section 1.2, we reviewed the original DCP-based dehazing algorithm [10]

Summary

Review

1.1 Introduction Due to absorption and scattering by atmospheric particles in haze, outdoor images have poor visibility under inclement weather. 1.2.2 Dark channel prior (DCP) He et al [10] performed an empirical investigation of the characteristic of haze-free outdoor images. They found that there are dark pixels whose intensity values are very close to zero for at least one color channel within an image patch. Global atmospheric light tends to be achromatic and bright, and a mixture of airlight and direct attenuation significantly increases the minimum value of the three color channels in the local patch This implies that the pixel values of the dark channel can serve as an important clue to estimate the haze density. 1.3.1 Dark channel construction Most conventional DCP-based dehazing methods estimate the dark channel from the input hazy image I. The effect of the size of the local patch is significant, most conventional methods use a local patch with a fixed size

Method

Transmission map estimation

Findings

Conclusions