scholarly journals An Image Dehazing Model considering Multiplicative Noise and Sensor Blur

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
P. Jidesh ◽  
A. A. Bini
Keyword(s):  
Multiplicative Noise ◽  
Imaging Systems ◽  
Second Step ◽  
Single Image ◽  
Image Dehazing ◽  
Step Model ◽  
Dark Channel ◽  
The Common ◽  
Restoration Model ◽  
Common Device

A restoration model considering the data-dependent multiplicative noise, shift-invariant blur, and haze has been introduced in this paper. The proposed strategy adopts a two-step model to perform a single image dehazing under the blurred and noisy observations. The first step uses the well-known dark channel prior method to estimate the transmission of the medium and atmospheric light that signifies the global color of the haze and dehaze the images. The second step performs denoising and deblurring under a Gamma distributed noise setup and a linear blurring artefact. The restoration under the above mentioned setup has quite a few applications in satellite and long-distant telescopic imaging systems, where the captured images are noisy due to atmospheric pressure turbulence and hazy due to the presence of atmospheric dust formation; further they are blurred due to the common device artefacts. The proposed strategy is tested using a large amount of available image-sets and the performance of the model is analysed in detail in the results section.

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

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.

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

Single Image Dehazing with Dark Channel Prior

2021 ◽  
Author(s):  
Jaspreet Kaur ◽  
Srishti Sabharwal ◽  
Ayush Dogra ◽  
Bhawna Goyal ◽  
Rohit Anand
Keyword(s):  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

Single Image Dehazing Algorithm Based on Adaptive Dark Channel Prior

2018 ◽  
Vol 47 (2) ◽  
pp. 210001
Author(s):  
刘国 LIU Guo ◽  
吕群波 L Qun bo ◽  
刘扬阳 LIU Yang yang
Keyword(s):  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing
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