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.

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.

A Fast Method for Defogging of Outdoor Visual Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Tannistha Pal
Keyword(s):  
Intelligent Vehicles ◽  
Visual Surveillance ◽  
Qualitative Assessment ◽  
Computational Time ◽  
Data Sets ◽  
Fast Method ◽  
Time Data ◽  
Image Defogging ◽  
Clear Vision ◽  
Dark Channel

Images captured in severe atmospheric catastrophe especially in fog critically degrade the quality of an image and thereby reduces the visibility of an image which in turn affects several computer vision applications like visual surveillance detection, intelligent vehicles, remote sensing, etc. Thus acquiring clear vision is the prime requirement of any image. In the last few years, many approaches have been made towards solving this problem. In this article, a comparative analysis has been made on different existing image defogging algorithms and then a technique has been proposed for image defogging based on dark channel prior strategy. Experimental results show that the proposed method shows efficient results by significantly improving the visual effects of images in foggy weather. Also computational time of the existing techniques are much higher which has been overcame in this paper by using the proposed method. Qualitative assessment evaluation is performed on both benchmark and real time data sets for determining theefficacy of the technique used. Finally, the whole work is concluded with its relative advantages and shortcomings.

scholarly journals Hybrid Approach for Wood Modification: Characterization and Evaluation of Weathering Resistance of Coatings on Acetylated Wood

Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 658
Author(s):  
Anna Sandak ◽  
Edit Földvári-Nagy ◽  
Faksawat Poohphajai ◽  
Rene Herrera Diaz ◽  
Oihana Gordobil ◽  
...  
Keyword(s):  
Service Life ◽  
Hybrid Approach ◽  
Wood Properties ◽  
Wood Products ◽  
Superior Performance ◽  
Surface Erosion ◽  
Coating Film ◽  
Chemical Treatments ◽  
Cellular Wall ◽  
Bulk Substrate

Wood, as a biological material, is sensitive to environmental conditions and microorganisms; therefore, wood products require protective measures to extend their service life in outdoor applications. Several modification processes are available for the improvement of wood properties, including commercially available solutions. Among the chemical treatments, acetylation by acetic anhydride is one of the most effective methods to induce chemical changes in the constitutive polymers at the cellular wall level. Acetylation reduces wood shrinkage-swelling, increases its durability against biotic agents, improves UV resistance and reduces surface erosion. However, even if the expected service life for external cladding of acetylated wood is estimated to be 60 years, the aesthetics change rapidly during the first years of exposure. Hybrid, or fusion, modification includes processes where the positive effect of a single treatment can be multiplied by merging with additional follow-up modifications. This report presents results of the performance tests of wood samples that, besides the modification by means of acetylation, were additionally protected with seven commercially available coatings. Natural weathering was conducted in Northern Italy for 15 months. Samples were characterized with numerous instruments by measuring samples collected from the stand every three months. Superior performance was observed on samples that merged both treatments. It is due to the combined effect of the wood acetylation and surface coating. Limited shrinkage/swelling of the bulk substrate due to chemical treatment substantially reduced stresses of the coating film. Hybrid process, compared to sole acetylation of wood, assured superior visual performance of the wood surface by preserving its original appearance.

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.

An improved dark channel prior image defogging algorithm based on wavelength compensation

2019 ◽  
Vol 12 (4) ◽  
pp. 501-512
Author(s):  
Zhixiang Chen ◽  
Binna Ou ◽  
Qianyi Tian
Keyword(s):  
Dark Channel Prior ◽  
Image Defogging ◽  
Dark Channel

scholarly journals A Robust Diffusion Minimum Kernel Risk-Sensitive Loss Algorithm over Multitask Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2339 ◽  
Author(s):  
Xinyu Li ◽  
Qing Shi ◽  
Shuangyi Xiao ◽  
Shukai Duan ◽  
Feng Chen
Keyword(s):  
Sensor Networks ◽  
Gaussian Noise ◽  
Impulsive Noise ◽  
Estimation Algorithm ◽  
Distributed Estimation ◽  
Superior Performance ◽  
Mean Square ◽  
Unknown Parameters ◽  
Risk Sensitive ◽  
The Impact

Distributed estimation over sensor networks has attracted much attention due to its various applications. The mean-square error (MSE) criterion is one of the most popular cost functions used in distributed estimation, which achieves its optimality only under Gaussian noise. However, impulsive noise also widely exists in real-world sensor networks. Thus, the distributed estimation algorithm based on the minimum kernel risk-sensitive loss (MKRSL) criterion is proposed in this paper to deal with non-Gaussian noise, particularly for impulsive noise. Furthermore, multiple tasks estimation problems in sensor networks are considered. Differing from a conventional single-task, the unknown parameters (tasks) can be different for different nodes in the multitask problem. Another important issue we focus on is the impact of the task similarity among nodes on multitask estimation performance. Besides, the performance of mean and mean square are analyzed theoretically. Simulation results verify a superior performance of the proposed algorithm compared with other related algorithms.

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