Research on dark channel dehazing of single-image based on non-dispersive infrared (NDIR) detection technology

2021 ◽  
pp. 1-8
Author(s):  
Xiangtian Zheng ◽  
Zhiyuan Xu
Keyword(s):  
Estimation Algorithm ◽  
Improve Image Quality ◽  
Detection Technology ◽  
Dark Channel ◽  
Change Characteristics ◽  
Value Estimation ◽  
Carbon Dioxide Detection ◽  
Weighting Coefficients ◽  
Atmospheric Light ◽  
Restoration Effect

This paper presents an experimental study on the non-dispersive infrared (NDIR) detection technology and dark channel dehazing technology. Based on the analysis of Beer-Lambert Law and differential carbon dioxide detection principle, this paper proposes an atmospheric light value estimation algorithm based on NDIR detection technology. First, the change characteristics of the gas concentration in indoor smoky environment are collected and analyzed. Then appropriate weighting coefficients are chosen based on the gas characteristics to estimate the atmospheric light value. Finally, the digital image dehazing technology through dark channel prior is used for calculation to obtain a haze-free image with high quality and high resolution. The experiment in this paper proves the feasibility of combining NDIR detection technology with dehazing technology, and its ability to improve image quality and achieve better restoration effect.

scholarly journals Hybrid Approach for Image Defogging Process based on Atmospheric Light Estimation Process

2021 ◽  
Vol 3 (3) ◽  
pp. 184-195
Author(s):  
Akey Sungheetha
Keyword(s):  
Hybrid Approach ◽  
Estimation Algorithm ◽  
Superior Performance ◽  
Statistical Process ◽  
Estimation Process ◽  
Multiple Sensors ◽  
Image Defogging ◽  
Dark Channel ◽  
Web Camera ◽  
Atmospheric Light

Due to unfavorable weather circumstances, images captured from multiple sensors have limited the contrast and visibility. Many applications, such as web camera surveillance in public locations are used to identify object categorization and capture a vehicle's licence plate in order to detect reckless driving. The traditional methods can improve the image quality by incorporating luminance, minimizing distortion, and removing unwanted visual effects from the given images. Dehazing is a vital step in the image defogging process of many real-time applications. This research article focuses on the prediction of transmission maps in the process of image defogging through the combination of dark channel prior (DCP), transmission map with refinement, and atmospheric light estimation process. This framework has succeeded in the prior segmentation process for obtaining a better visualization. This prediction of transmission maps can be improved through the statistical process of obtaining higher accuracy for the proposed model. This improvement can be achieved by incorporating the proposed framework with an atmospheric light estimation algorithm. Finally, the experimental results show that the proposed deep learning model is achieving a superior performance when compared to other traditional algorithms.

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.

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 Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm

2020 ◽  
Vol 10 (3) ◽  
pp. 1190
Author(s):  
Samia Haouassi ◽  
Di Wu
Keyword(s):  
State Of The Art ◽  
Estimation Algorithm ◽  
Surveillance Systems ◽  
Security Systems ◽  
High Quality ◽  
Image Dehazing ◽  
Multi Scale ◽  
Second Stage ◽  
Computer Vision Applications ◽  
Atmospheric Light

Image dehazing plays a pivotal role in numerous computer vision applications such as object recognition, surveillance systems, and security systems, where it can be considered as an introductory stage. Recently, many proposed learning-based works address this significant task; however, most of them neglect the atmospheric light estimation and fail to produce accurate transmission maps. To address such a problem, in this paper, we propose a two-stage dehazing system. The first stage presents an accurate atmospheric light algorithm labeled “A-Est” that employs hazy image blurriness and quadtree decomposition. Te second stage represents a cascaded multi-scale CNN model called CMT n e t that consists of two subnetworks, one for calculating rough transmission maps (CMCNN t r ) and the other for its refinement (CMCNN t ). Each subnetwork is composed of three-layer D-units (D indicates dense). Experimental analysis and comparisons with state-of-the-art dehazing methods revealed that the proposed system can estimate AL and t efficiently and accurately by achieving high-quality dehazing results and outperforms state-of-the-art comparative methods according to SSIM and MSE values, where our proposed achieves the best scores of both (91% average SSIM and 0.068 average MSE).

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 Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior

Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 45
Author(s):  
Fan Yang ◽  
ShouLian Tang
Keyword(s):  
Estimation Algorithm ◽  
Third Party ◽  
Dark Channel Prior ◽  
Single Image ◽  
Tolerance Mechanism ◽  
Image Dehazing ◽  
Bright Region ◽  
Color Distortion ◽  
Direct Light ◽  
Dark Channel

The tolerance mechanism based on dark channel prior (DCP) of a single image dehazing algorithm is less effective when there are large areas of the bright region in the hazy image because it cannot obtain the tolerance adaptively according to the characteristics of the image. It will lead to insufficient improvement of the transmission of image, so it is difficult to eliminate the color distortion and block effects in the restored image completely. Moreover, when a dense haze area or a third-party direct light source (sunlight, headlights and reflected glare) is misjudged as sky area, the use of tolerance will cause an inferior dehazing effect such as details lost. Regarding the issue above, this paper proposes an adaptive tolerance estimation algorithm. The tolerance is obtained according to the statistic characteristics of each image to make the estimation of transmission more accurately. The experimental results show that the proposed algorithm not only maintains high operational efficiency but also effectively compensates for the defects of the dark channel prior to some scenes. The proposed algorithm can effectively solve the problem of color distortion recovered by the DCP method in the bright regions of the image.

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.

Carbon dioxide detection technology based on the laser occultation absorption spectrum

2021 ◽  
Vol 0 (0) ◽  
pp. 1-9
Author(s):  
WANG Yu-Zhao ◽  
◽  
◽  
TAO Yu-Liang ◽  
SUN Hai-Qing ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Absorption Spectrum ◽  
Detection Technology ◽  
Carbon Dioxide Detection
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