Image Dehazing and Enhancement: A Review

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Mohit Kumar Verma ◽  
Permendra Kumar Verma
Keyword(s):  
Machine Vision ◽  
Processing Method ◽  
Video Monitoring ◽  
Degradation Mechanisms ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Channel Theory ◽  
Tracking And Recognition ◽  
Atmospheric Light

The enhancement of images is an image processing method that highlights certain image information according to specific needs and, at the same time, weakens or removes unwanted information. In the field of computer and machine vision, haziness and fog lead to degradation of images using different degradation mechanisms, including contrast attenuation, blurring, and degradation of the pixels. This limits machine vision systems efficiency such as video monitoring, target tracking, and recognition. Different dark channel single image dehazing algorithms have been designed quickly and efficiently to address image hazing problems. These algorithms rely on the dark channel theory to estimate the atmospheric light which is a crucial dehazing parameter. In this paper, a review of image dehazing and enhancement has been presented.

Single-Image Dehazing via Dark Channel Prior and Adaptive Threshold

2021 ◽  
pp. 2150053
Author(s):  
Yongpeng Pan ◽  
Zhenxue Chen ◽  
Xianming Li ◽  
Weikai He
Keyword(s):  
Adaptive Threshold ◽  
Dark Channel Prior ◽  
Segmentation Method ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Traditional Algorithm ◽  
Computer Vision Systems ◽  
Atmospheric Light ◽  
Single Image Dehazing

Due to the haze weather, the outdoor image quality is degraded, which reduces the image contrast, thereby reducing the efficiency of computer vision systems such as target recognition. There are two aspects of the traditional algorithm based on the principle of dark channel to be improved. First, the restored images obviously contain color distortion in the sky region. Second, the white regions in the scene easily affect the atmospheric light estimated. To solve the above problems, this paper proposes a single-image dehazing and image segmentation method via dark channel prior (DCP) and adaptive threshold. The sky region of hazing image is relatively bright, so sky region does not meet the DCP. The sky part is separated by the adaptive threshold, then the scenery and the sky area are dehazed, respectively. In order to avoid the interference caused by white objects to the estimation of atmospheric light, we estimate the value of atmospheric light using the separated area of the sky. The algorithm in this paper makes up for the shortcoming that the algorithm based on the DCP cannot effectively process the hazing image with sky region, avoiding the effect of white objects on estimating atmospheric light. Experimental results show the feasibility and effectiveness of the improved algorithm.

scholarly journals A Fast Single-Image Dehazing Algorithm Based on Dark Channel Prior and Rayleigh Scattering

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 73330-73339 ◽  
Author(s):  
Jehoiada Jackson ◽  
She Kun ◽  
Kwame Obour Agyekum ◽  
Ariyo Oluwasanmi ◽  
Parinya Suwansrikham
Keyword(s):  
Rayleigh Scattering ◽  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

Hybrid single image dehazing with bright channel and dark channel priors

2017 ◽  
Author(s):  
Jehoiada Jackson ◽  
Oluwasanmi Ariyo ◽  
Kingsley Acheampong ◽  
Maxwell Boakye ◽  
Enoch Frimpong ◽  
...  
Keyword(s):  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

scholarly journals Restoration and Enhancement of Underwater Images Based on Bright Channel Prior

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yakun Gao ◽  
Haibin Li ◽  
Shuhuan Wen
Keyword(s):  
Histogram Equalization ◽  
New Method ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Underwater Environment ◽  
Dark Channel ◽  
Bright Channel Prior ◽  
Channel Image ◽  
Atmospheric Light

This paper proposed a new method of underwater images restoration and enhancement which was inspired by the dark channel prior in image dehazing field. Firstly, we proposed the bright channel prior of underwater environment. By estimating and rectifying the bright channel image, estimating the atmospheric light, and estimating and refining the transmittance image, eventually underwater images were restored. Secondly, in order to rectify the color distortion, the restoration images were equalized by using the deduced histogram equalization. The experiment results showed that the proposed method could enhance the quality of underwater images effectively.

scholarly journals Single Image Dehazing Using Sparse Contextual Representation

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1266
Author(s):  
Jing Qin ◽  
Liang Chen ◽  
Jian Xu ◽  
Wenqi Ren
Keyword(s):  
Sparse Representation ◽  
State Of The Art ◽  
Input Image ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Local Patch ◽  
Novel Method ◽  
Contextual Representation ◽  
Single Image Dehazing

In this paper, we propose a novel method to remove haze from a single hazy input image based on the sparse representation. In our method, the sparse representation is proposed to be used as a contextual regularization tool, which can reduce the block artifacts and halos produced by only using dark channel prior without soft matting as the transmission is not always constant in a local patch. A novel way to use dictionary is proposed to smooth an image and generate the sharp dehazed result. Experimental results demonstrate that our proposed method performs favorably against the state-of-the-art dehazing methods and produces high-quality dehazed and vivid color results.

scholarly journals Single Image Dehazing Algorithm Based on Modified Dark Channel Prior

2021 ◽  
Vol E104.D (10) ◽  
pp. 1758-1761
Author(s):  
Hao ZHOU ◽  
Zhuangzhuang ZHANG ◽  
Yun LIU ◽  
Meiyan XUAN ◽  
Weiwei JIANG ◽  
...  
Keyword(s):  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing
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