• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.212-218
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• 2022

The performance of vision-based intelligent systems, such as self-driving cars and unmanned aerial vehicles, is subject to weather conditions, notably the frequently encountered haze or fog. As a result, studies on haze removal have garnered increasing interest from academia and industry. This paper hereby presents a 4K-capable hardware implementation of an efficient haze removal algorithm with the following two improvements. First, the depth-dependent haze distribution is predicted using a linear model of four haze-relevant features, where the model parameters are obtained through maximum likelihood estimates. Second, the approximated quad-decomposition method is adopted to estimate the atmospheric light. Extensive experimental results then follow to verify the efficacy of the proposed algorithm against well-known benchmark methods. For real-time processing, this paper also presents a pipelined architecture comprised of customized macros, such as split multipliers, parallel dividers, and serial dividers. The implementation results demonstrated that the proposed hardware design can handle DCI 4K videos at 30.8 frames per second.

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.10-21
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• 2016

Images taken from outdoor are degraded quality by fog or haze, etc. In this paper, we propose a method that provides the visibility improved images through fog or haze removal. We proposed haze removal method that uses brightness transform based on atmospheric scatter coefficient rate with local histogram equalization. To calculate the transmission rate that indicate fog rate in original image, we use atmospheric scatter coefficient rate based on quadratic equations about haze model. And primary brightness transformed image can be obtained by using the obtained transmission rate. Also we use local histogram equalization with proposed brightness transform for effectively image visibility enhancement. Unlike existing methods, our method can process real-time with stable and effect image visibility enhancement. Proposed method use only the luminance images processed by good performance surveillance systems because it represents the real-time processing is required, black-box, digital camera and multimedia equipment is applicable. Also because it shows good performance only with the luminance images processed, Surveillance systems, black boxes, digital cameras, and multimedia devices etc, that require real-time processing can be applied.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.1
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• pp.73-86
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• 2011

Haze phenomena were analyzed to assess the impact of long range transport process on the air quality of Seoul and Busan. We statistically classified haze days observed in both Seoul and Busan into two types of haze cases: stagnant case and long-range transport case, and analyzed the air pollutant levels comparatively for each of the two cases for the period of 2000~2007. The results showed that the long-range transport haze case occurs less frequently with the occurrence frequency of 35.5% than stagnant case with the occurrence frequency of 64.5%. During the observed all haze days, all pollutants have high concentration in comparison with those under other meteorological conditions (Rain, Mist, Dust, Clear, Rain+Mist) except for only $PM_{10}$ of Dust case where its level shows highest among total 6 categorized conditions. The long range transport haze case shows similar levels of $PM_{10}$ and $NO_2$, but higher $SO_2$ and lower $O_3$ compared with stagnant haze cases, suggesting the importance of sulfur chemistry for long range transport haze case and local photochemistry for stagnant haze case. In addition, by employing the NOAA/HYSPLIT-4 backward trajectory model, we subdivided the long range transport haze cases into two different sources: urban anthropogenic high emission areas of central China, and natural emission sources over north China and/or Mongolia. The former long range transport haze case shows higher occurrence (with Seoul 70% and Busan 85%) than the latter haze case (with Seoul 30% and Busan <10%). This is also implying that the long haze phenomena occurred over Korea have been influenced by not only the anthropogenic emissions but also the natural dust emissions. These both emission sources can be good contributors in calculating the source-receptor relationship over Korean atmospheric environment.

• Journal of the Korean institute of surface engineering
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• v.51 no.5
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• pp.303-308
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• 2018

Currently hydrofluoric acid (HF) based glass etch method is widely used for anti-glare (AG) surface treatment since it can effectively alleviate the specular reflection problem with relatively low processing cost. However, due to the environmental regulation and safety problem, it is essential to develop alternative technology to replace this method. For this, in this paper, we propose sand blasting based AG surface treatment method for display glass. To characterize the sand blasting process, surface roughness, haze, surface durability, and flatness are considered as process outputs and central composite design (CCD) method and response surface model (RSM) method are applied to model each process output. Models for surface roughness and haze showed 96.44% and 97.24% of R-squared values, respectively and they can be applied to optimize AG surface treatment process for various haze level requirements of display industries.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.45-54
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• 2022

Image depth estimation is a technology that is the basis of various image analysis. As analysis methods using deep learning models emerge, studies using deep learning in image depth estimation are being actively conducted. Currently, most deep learning-based depth estimation models are being trained with clean and ideal images. However, due to the lack of data on adverse conditions such as haze or fog, the depth estimation may not work well in such an environment. It is hard to sufficiently secure an image in these environments, and in particular, obtaining non-homogeneous haze data is a very difficult problem. In order to solve this problem, in this study, we propose a method of synthesizing non-homogeneous haze images and a learning method for a monocular depth estimation deep learning model using this method. Considering that haze mainly occurs outdoors, datasets mainly containing outdoor images are constructed. Experiment results show that the model with the proposed method is good at estimating depth in both synthesized and real haze data.

• Journal of Platform Technology
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• v.11 no.6
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• pp.68-78
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• 2023

In this paper, the color distortion in images with haze would be analyzed. When haze is included in the scene, the color signal reflected in the scene is accompanied by color distortion due to the influence of transmittance according to the haze component. When the influence of haze is excluded by a conventional de-hazing method, the distortion of color tends to not be sufficiently resolved. Khoury et al. used the dark channel priority technique, a haze model mentioned in many studies, to determine the degree of color distortion. However, only the tendency of distortion such as color error values was confirmed, and specific color distortion analysis was not performed. This paper analyzes the characteristic of color distortion and proposes a restoration method that can reduce color distortion. Input images of databases used by Khoury et al. include Macbeth color checker, a standard color tool. Using Macbeth color checker's color values, color distortion according to changes in haze concentration was analyzed, and a new color distortion model was proposed through modeling. The proposed method is to obtain a mapping function using the change in chromaticity by step according to the change in haze concentration and the color of the ground truth. Since the form of color distortion varies from step to step in proportion to the haze concentration, it is necessary to obtain an integrated thought function that operates stably at all stages. In this paper, the improvement of color distortion through the proposed method was estimated based on the value of angular error, and it was verified that there was an improvement effect of about 15% compared to the conventional method.

• Journal of IKEEE
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• v.24 no.2
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• pp.492-496
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• 2020

The visibility deteriorates in hazy weather and it is difficult to accurately recognize information captured by the camera. Research is being actively conducted to remove haze so that camera-based applications such as object localization/detection and lane recognition can operate normally even in hazy weather. In this paper, we propose a nonlinear model for depth map estimation through an extensive analysis that the difference between brightness and saturation in hazy image increases non-linearly with the depth of the image. The quantitative evaluation(MSE, SSIM, TMQI) shows that the proposed haze removal method based on the nonlinear model is superior to other state-of-the-art methods.

• Journal of IKEEE
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• v.25 no.1
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• pp.83-87
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• 2021

In harsh environments such as fog or fine dust, the cameras' detection ability for object recognition may significantly decrease. In order to accurately obtain important information even in bad weather, fog removal algorithms are necessarily required. Research has been conducted in various ways, such as computer vision/data-based fog removal technology. In those techniques, estimating the amount of fog through the input image's depth information is an important procedure. In this paper, a linear model is presented under the assumption that the image dark channel dictionary, saturation ∗ value, and sharpness characteristics are linearly related to depth information. The proposed method of haze removal through a linear model shows the superiority of algorithm performance in quantitative numerical evaluation.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.229-234
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• 2020

Dehazing significantly improves image quality by restoring the loss of contrast and color saturation for images taken in the presence. However, when applied to images not taken according to the prior information, dehazing can cause unintended degradation of image quality. To avoid unintended degradations, we present a hazy scene detection algorithm using a single image based on the distributions of hue, saturation, and dark channel. Through a heuristic approach, we find out statistical characteristics of the distribution of hue, saturation, and dark channels in the hazy scene and make a detection model using them. The proposed method can precede the dehazing to prevent unintended degradation. The detection performance evaluated with a set of test images shows a high hit rate with a low false alarm ratio. Ultimately the proposed method can be used to control the effect of dehazing so that the dehazing can be applied to wide variety of images without unintended degradation of image quality.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.656-664
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• 2014

In the case of a haze image, transferring the information of the original image is difficult as the contrast leans toward bright regions. Thus, dehazing algorithms have become an important area of study. Normally, since it is hard to obtain a haze-free image, the output image is qualitatively analyzed to test the performance of an algorithm. However, this paper proposes a quantitative error comparison based on reproducing the haze image using a disparity map. In addition, a Hidden Random Markov Model and EM algorithm are used to remove any block artifacts. The performance of the proposed algorithm is confirmed using a variety of synthetic and natural images.