• Journal of Korea Multimedia Society
• /
• v.25 no.9
• /
• pp.1266-1272
• /
• 2022

With the recent development of computer vision technology, image processing-based mechanical devices are being developed to realize autonomous driving. The camera-taken images of image processing-based machines are invisible due to scattering and absorption of light in foggy conditions. This lowers the object recognition rate and causes malfunction. The safety of the technology is very important because the malfunction of autonomous driving leads to human casualties. In order to increase the stability of the technology, it is necessary to apply an efficient haze removal algorithm to the camera. In the conventional haze removal method, since the haze removal operation is performed regardless of the haze concentration of the input image, excessive haze is removed and the quality of the resulting image is deteriorated. In this paper, we propose an automatic haze removal method that removes haze according to the haze density of the input image by applying Ngo's Haziness Degree Evaluator (HDE) to Kim's haze removal algorithm using Hazy Particle Map. The proposed haze removal method removes the haze according to the haze concentration of the input image, thereby preventing the quality degradation of the input image that does not require haze removal and solving the problem of excessive haze removal. The superiority of the proposed haze removal method is verified through qualitative and quantitative evaluation.

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

• Asian Journal of Atmospheric Environment
• /
• v.10 no.4
• /
• pp.226-231
• /
• 2016

In order to remove fog often causes various troubles in our daily lives, the novel defog blower equipped the roller brush made of recycled horse's mane hair was developed. This work presents the overview of new defog devices and the experimental data obtained at two different kinds of defogging experiments. In the model experiment carried out at the enclosed cleanroom ($W5.9m{\times}L5.1m{\times}H2.4m$) targeted a vinyl house, fog was dissipated in less than 30 seconds in case with wind entrainment and two minutes 45 seconds in case without wind entrainment after running of the newly designed defog blower. When the demisting blower was run in a duct, it has an excellent mist sweeping qualities as well as a great removal effect for the background particles (89.5% and 65.4% scavenging rates for fine and coarse particles, respectively). It can be therefore said that the mist eliminator presented in this paper is ideal for use in the sealing space like a vinyl house and the industrial sites where required to remove both harmful mist and particle.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.1
• /
• pp.96-110
• /
• 2017

Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2019.11a
• /
• pp.120-121
• /
• 2019

The construction industry is known to be one of the representative industries that generate fine dust. Therefore, reducing the amount of fine dust generated in construction sites is very important for the overall fine dust management. Based on this, this study proposed the concept of fine dust measurement and removal technology combined with advanced technologies such as drones and IoT. The qualitative, quantitative and risk elimination effects that can be expected when applying the proposed technique are analyzed. We will verify the effectiveness of the proposed concept through system development and field application, and evaluate specific economic feasibility through cost analysis. The proposed concept will be validated through system development and field application and evaluated specific economics through cost analysis.

• Journal of IKEEE
• /
• v.26 no.3
• /
• pp.401-407
• /
• 2022

Recently, image processing technology for autonomous driving by recognizing objects and lanes through camera images to realize autonomous vehicles is being studied. Haze reduces the visibility of images captured by the camera and causes malfunctions of autonomous vehicles. To solve this, it is necessary to apply the haze removal function that can be processed in real time to the camera. Therefore, in this paper, the fog removal method of Sim with excellent performance is implemented with hardware capable of real-time processing. The proposed hardware was designed using Verilog HDL, and FPGA was implemented by setting Xilinx's xc7z045-2ffg900 as the target device. As a result of logic synthesis using Xilinx Vivado program, it has a maximum operating frequency of 276.932MHz and a maximum processing speed of 31.279fps in a 4K (4096×2160) high-resolution environment, thus satisfying the real-time processing standard.

• Archives of Plastic Surgery
• /
• v.48 no.4
• /
• pp.353-360
• /
• 2021

Background Despite evidence supporting the safety of breast implants, some women associate their implants with adverse health effects and have called this syndrome "breast implant illness." We sought to characterize breast implant illness symptoms and to report how implant removal affects their symptoms. Methods An anonymous 20 question survey was administered to the Facebook group: "UTAH Breast Implant Illness" to characterize the symptoms these women attributed to their breast implants. Several questions allowed us to evaluate how implant removal affected women's symptoms. Results Of the 182 respondents, 97% report that implants negatively affect their health and 95% identify these symptoms with breast implant illness. Ninety-six percent of respondents had implants placed for cosmetic reasons and 51% had silicone implants. The most common symptoms associated with breast implant illness are brain fog (95%), fatigue (92%), joint pain (80%), and hair loss (74%). Sixty percent of respondents learned about breast implant illness from family/friends and/or social media platforms (56%), 40% of respondents had their implants removed, and 97% report relief of their symptoms post-removal (23% complete, 74% partial). Following explantation, there was a significant improvement in all but one reported symptom. An association was found between the number of symptoms reported prior to explantation and the number of symptoms resolving following explantation. Conclusions Breast implant illness is a syndrome characterized by fatigue, decreased focus, hair loss, and joint pain after the placement of breast implants. Nearly all patients report improvement of symptoms after implant removal. Significant efforts should be made to better understand breast implant illness and its etiology.

• Journal of the Korea Computer Graphics Society
• /
• v.28 no.4
• /
• pp.23-30
• /
• 2022

In this paper, we propose an algorithm that can accurately segment a fire even when it is surrounded by smoke of a similar color. Existing fire area segmentation algorithms have a problem in that they cannot separate fire and smoke from fire images. In this paper, the fire was successfully separated from the smoke by applying the color compensation method and the fog removal method as a preprocessing process before applying the fire area segmentation algorithm. In fact, it was confirmed that it segments fire more effectively than the existing methods in the image of the fire scene covered with smoke. In addition, we propose a method that can use the proposed fire segmentation algorithm for efficient fire detection in factories and homes.

• Journal of Broadcast Engineering
• /
• v.18 no.2
• /
• pp.140-148
• /
• 2013

The haze removal algorithm using median dark channel prior is an efficient and fast method with relatively accurate transmission estimation. However, conventional methods may produce color distortion since the method ignores the color mismatch between estimated airlight and actual airlight. In this paper, we propose a color correction with measuring color fidelity in the HSI color space. Experimental results show that the proposed algorithm gives better color correction scheme.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.5
• /
• pp.168-176
• /
• 2014

Detecting moving objects from a video sequence is a fundamental and critical task in video surveillance, traffic monitoring and analysis, and human detection and tracking. It is very difficult to detect moving objects in a video sequence degraded by the environmental factor such as fog. In particular, the color of an object become similar to the neighbor and it reduces the saturation, thus making it very difficult to distinguish the object from the background. For such a reason, it is shown that the performance and reliability of object detection and tracking are poor in the foggy weather. In this paper, we propose a novel method to improve the performance of object detection, combining a haze removal algorithm and a local histogram-based object tracking method. For the quantitative evaluation of the proposed system, information retrieval measurements, recall and precision, are used to quantify how well the performance is improved before and after the haze removal. As a result, the visibility of the image is enhanced and the performance of objects detection is improved.