Image dehazing based on dark channel spatial stimuli gradient model and image morphology

2020 ◽  
Author(s):  
Rehan Mehmood Yousaf ◽  
Hafiz Adnan Habib ◽  
Zahid Mehmood ◽  
Muhammad Bilal
Keyword(s):  
Image Dehazing ◽  
Gradient Model ◽  
Dark Channel ◽  
Image Morphology

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

scholarly journals An image dehazing algorithm based on double priors constraint

2018 ◽  
Vol 189 ◽  
pp. 04009
Author(s):  
Kun Liu ◽  
Shiping Wang ◽  
Linyuan He ◽  
Duyan Bi ◽  
Shan Gao
Keyword(s):  
Standard Deviation ◽  
Mean Value ◽  
Experimental Results ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Color Distortion ◽  
Local Media ◽  
Dark Channel

Aiming at the color distortion of the restored image in the sky region, we propose an image dehazing algorithm based on double priors constraint. Firstly, we divided the haze image into sky and non-sky regions. Then the Color-lines prior and dark channel prior are used for estimating the transmission of sky and non-sky regions respectively. After introducing color-lines prior to correct sky regions restored by the dark channel prior, we get an accurate transmission. Finally, the local media mean value and standard deviation are used to refine the transmission to obtain the dehazing image. Experimental results show that the algorithm has obvious advantages in the recovery of the sky area.

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 Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-World Hazy Scenes

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 960
Author(s):  
Zhan Li ◽  
Jianhang Zhang ◽  
Ruibin Zhong ◽  
Bir Bhanu ◽  
Yuling Chen ◽  
...  
Keyword(s):  
Real World ◽  
Evaluation Criteria ◽  
Synthetic Data ◽  
Network Size ◽  
Image Dehazing ◽  
Depth Data ◽  
Network Training ◽  
Dark Channel ◽  
Processing Techniques ◽  
Transmission Estimation

In this paper, a transmission-guided lightweight neural network called TGL-Net is proposed for efficient image dehazing. Unlike most current dehazing methods that produce simulated transmission maps from depth data and haze-free images, in the proposed work, guided transmission maps are computed automatically using a filter-refined dark-channel-prior (F-DCP) method from real-world hazy images as a regularizer, which facilitates network training not only on synthetic data, but also on natural images. A double-error loss function that combines the errors of a transmission map with the errors of a dehazed image is used to guide network training. The method provides a feasible solution for introducing priors obtained from traditional non-learning-based image processing techniques as a guide for training deep neural networks. Extensive experimental results demonstrate that, in terms of several reference and non-reference evaluation criteria for real-world images, the proposed method can achieve state-of-the-art performance with a much smaller network size and with significant improvements in efficiency resulting from the training guidance.

Genetic Algorithm-based Dark Channel Prior Parameters Selection for Single Underwater Image Dehazing

2020 ◽  
Author(s):  
Vincent Jan D. Almero ◽  
Ronnie S. Concepcion ◽  
Jonnel D. Alejandrino ◽  
Argel A. Bandala ◽  
Jason L. Espanola ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Underwater Image ◽  
Parameters Selection ◽  
Dark Channel ◽  
Selection For

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.

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.

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