A review on dark channel prior based image dehazing algorithms

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.

Underwater image and video dehazing with pure haze region segmentation

“…Therefore dark channel-based veiling light estimation is biased towards pixels from bright objects in the scene that are larger than the size of the local patch used to generate the dark channel. Large patches help to avoid inaccurate estimates caused by bright pixels which invalidate the prior, while small patches help to maintain image details during the generation of the transmission map ( Lee et al, 2016 ). To ensure both global and local information is taken into account we employ a hierarchical veiling light estimation method ( Emberton et al, 2015 ), which fuses a range of layers with different-sized patches.…”

A Visibility Restoration Algorithm for Real-World Hazy Scenes

“…These existing methods with multiple reference images have limitations in online dehazing applications and may need a special imaging sensor [12]. It provided a lead the researchers to focus the dehazing method with a single reference image.…”

“…Moreover, most automatic systems were strongly depended upon the definition of the participation images and unsuccessful to work typically reason by the tainted images. Therefore, the technique of haze removal can benefit in several image understanding and pc-vision applications like aerial imagery [1], image classification [2]- [5], image or video retrieval [6]- [8], remote sensing and video analysis and recognition [12]- [14].…”

Research on the Method to Recover Color of the Image Influenced by Atmospheric Light Curtains

“…The atmosphere will produce scattering and absorption attenuation when light from objects to receiver in the transmission process, while attached to the air curtain brightness related to the atmospheric transmission distance in the transmission direction, making the visible light brightness and color characteristics change [6].…”