scholarly journals Image Dehazing Based on Physical Model and Gray Projection

2019 ◽  
Vol 9 (1S3) ◽  
pp. 241-245
Keyword(s):  
Image Restoration ◽  
Physical Model ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Dark Channel ◽  
Restoration Techniques ◽  
Image Computation ◽  
Computing Speed ◽  
Atmospheric Light

The climatic scattering and ingestion offer climb to the ordinary marvel of obscurity, which truly impacts the detectable quality of view. Dehazing is the technique used to expel the dimness. In late year, various works have been done to improve the detectable quality of picture taken under horrible climate. The images that are taken under overcast conditions experience the evil impacts of shading contortion and attenuation. The proposed strategy is in light of the Dark Channel Prior speculation and gray projection. The transmission map is resolved using the determined estimation of atmospheric light. It uses box filter to lessen the complexity and to improve the computing speed. This computation can restore image with incredible quality and the speed of image computation is high. The proposed strategy is differentiated with other image enhancement strategies and image restoration techniques. It is likewise exceptionally proficient technique since it can process huge images within less time.

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.

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 FPGA Implementation of Video Dehazing using Dark Channel Priori Algorithm

2020 ◽  
Vol 8 (5) ◽  
pp. 1875-1878
Keyword(s):  
Fpga Implementation ◽  
Atmospheric Parameters ◽  
Matlab Simulation ◽  
Color Information ◽  
Image Dehazing ◽  
Edge Information ◽  
Dark Channel ◽  
Restoration Techniques ◽  
The Cost

The images captured by the camera are dependent on the illumination and reflectance components. The quality of images is degraded by the atmospheric parameters such as poor illumination intensity, rain, haze and fog. The images affected by fog and haze generally lose edge and color information. The image restoration techniques such as dehazing help in retrieving the edge information, but at the cost of color information. The image enhancement such as image dehazing using a dark channel priori algorithm is performed on the image to improve the information content in the image. In this paper, we propose a method of FPGA implementation of video dehazing using a dark channel priori algorithm. The proposed architecture is implemented using VHDL in Cyclone III FPGA with an operating frequency of 108 Hz. The results of the dark channel priori method are verified with the MATLAB simulation results.

scholarly journals Dark Channel Prior Based Image Dehazing using Different Transmission Map Refinement Methods

2019 ◽  
Vol 8 (12) ◽  
pp. 3015-3020
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Ground Truth ◽  
Bilateral Filter ◽  
Dark Channel Prior ◽  
Blocking Artifacts ◽  
Image Dehazing ◽  
Data Set ◽  
Dark Channel ◽  
Atmospheric Light

Due to existence of haze, the image quality is degraded in the environment. Removal of haze is called dehazing. To dehaze an image Dark Channel Prior is recommended. Dark Channel Prior is an observation, that an image has few pixels whose intensity value is very small or near to zero in most non-sky patches. Such pixels are referred to as dark pixels. Dehazing through Dark Channel Prior is accomplished using four major steps. The steps include estimating atmospheric light, estimating transmission map, refinement of transmission map and image reconstruction. Incorrect estimation of transmission map may lead to some problems. These problems include false textures and blocking artifacts. Many methods are developed to further sharpen transmission map. Here transmission map is refined using soft matting, guided filter and bilateral filter. The comparison of dehazing methods has become difficult due to scarce availability of ground truth images .So we used I-HAZE, a new data set containing 35 picture pairs of hazy pictures and their respective ground truth pictures. A significant benefit of I-HAZE data set is that it allows us to compare different refinement methods used for dehazing with SSIM, PSNR and RMSE which are used for the measurement of finally obtained reconstructed image quality after the removal of haze.

Single image dehazing with a physical model and dark channel prior

2015 ◽  
Vol 149 ◽  
pp. 718-728 ◽  
Author(s):  
Jin-Bao Wang ◽  
Ning He ◽  
Lu-Lu Zhang ◽  
Ke Lu
Keyword(s):  
Physical Model ◽  
Dark Channel Prior ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Single Image Dehazing

scholarly journals Haze Removal System using Dark Channel Prior

2021 ◽  
Vol 9 (VI) ◽  
pp. 301-309
Author(s):  
Disha M. Jaiswal
Keyword(s):  
Road Traffic ◽  
Winter Season ◽  
Road Transport ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Haze Removal ◽  
The Road ◽  
Dark Channel ◽  
Atmospheric Light ◽  
Removal System

Mostly in winter season, the Northern area of India is mostly affected due to heavy haze. The road traffic and air traffic is affected due to poor visibility. According to the survey of Ministry of Road Transport and Highways of India, the number of accident due to poor visibility increasing every year. Hence there is need of robust algorithm to enhance the visibility of the camera feed. In the proposed approach, image dehazing algorithm has been present using dark channel prior. The proposed algorithm is developed for outdoor images. The proposed system processed the image through dark channel prior, estimation of atmospheric light, estimation of transmission and scene radiance. The proposed system achieved the promising results on O-Haze dataset.

scholarly journals Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization

2021 ◽  
Vol 9 (6) ◽  
pp. 570
Author(s):  
Qingliang Jiao ◽  
Ming Liu ◽  
Pengyu Li ◽  
Liquan Dong ◽  
Mei Hui ◽  
...  
Keyword(s):  
Image Restoration ◽  
Qualitative And Quantitative Analysis ◽  
Formation Model ◽  
High Quality ◽  
Thermal Exchange ◽  
Qualitative And Quantitative ◽  
Smooth Variation ◽  
Underwater Image ◽  
Dark Channel

The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of underwater images. Finally, the restored image can be estimated via the image formation model. The results show that the proposed algorithm can output high-quality images, according to qualitative and quantitative analysis.

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
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