• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.437-440
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• 2022

In modern society, the use of the image processing field is increasing rapidly due to the 4th industrial revolution and the development of IoT technology. In particular, edge detection is widely used in various fields as an essential preprocessing process in image processing applications such as image classification and object detection. Conventional methods for detecting an edge include a Sobel edge detection filter, a Roberts edge detection filter, a Prewitt edge detection filter, Laplacian of Gaussian (LoG), and the like. However, existing methods have the disadvantage of showing somewhat insufficient performance of edge detection characteristics in a low-light level environment with low contrast. Therefore, this paper proposes an edge detection algorithm based on contrast analysis to increase edge detection characteristics even in low-light level environments.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.12
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• pp.691-697
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• 2000

A fiberoptic sensor using a low-coherence SLD as a light source has been studied. The sensor system employing an intrinsic fiber Fabry-Peort interferometer as a sensing tip and a fiber Mach-Zehnder interferometer as a processing one, overcomes the ambiguous reading caused by the highly periodic natrue of conventional high-precision interferometric sensors and provides unambiguous identification of the desired phase among several candidates on the transfer function of an interferometric signal. A tentative application to the temperature sensor shows the potential that the fiberoptic sensor has a side-dynamic range of $0-900^{\circ}C$ as well as reasonable resolution higher than $0.1^{\circ}C$ without ambiguity. Due to the inherent property of the optical fiber itself and the intrinsic fiber Fabry-Perot interferometer, the proposed fiberoptic sensor will give obvious benefits when it is applied to harsh environments to monitor some physical parameters such as temperature, strain, pressure and vibration.

• ETRI Journal
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• v.34 no.1
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• pp.138-141
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• 2012

This letter proposes a real-time digital image stabilization system for cell phone cameras without the need for frame memory. The system post-processes an image captured with a safe shutter speed using an adaptive denoising filter and a global color correction algorithm. This system can transfer the normal brightness of an image previewed under long exposure to the captured image making it bright and crisp with low noise. It is even possible to take photos in low-light conditions. By not needing frame memory, the approach is feasible for integration into the size-constrained image sensors of cell phone cameras.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.980-997
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• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1680-1686
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• 2015

Edge detection is an essential preprocessing for most image processing application, and there are several existing detection methods such as Sobel, Roberts, Laplacian, LoG(Laplacian of Gaussian) operators, etc. Those existing edge detection methods have not given satisfactory results since they do not offer enough pixel brightness change in low light level environment. Therefore, in this study new algorithms using brightness transfer function in the preprocessing and for edge detection applying standard deviation and average-weighted local masks are proposed. In addition, the performance of proposed algorithms was evaluated in comparison with the existing edge detection methods such as Sobel, Roberts, Prewitt, Laplacian, LoG operators.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.547-553
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• 2021

Dynamic object recognition is an important task for autonomous vehicles. Since dynamic objects exhibit a higher collision risk than static objects, our own trajectories should be planned to match the future state of moving elements in the scene. Time information such as optical flow can be used to recognize movement. Existing optical flow calculations are based only on camera sensors and are prone to misunderstanding in low light conditions. In this regard, to improve recognition performance in low-light environments, we applied a normalization filter and a correction function for Gamma Value to the input images. The low light quality improvement algorithm can be applied to confirm the more accurate detection of Object's Bounding Box for the vehicle. It was confirmed that there is an important in object recognition through image prepocessing and deep learning using YOLO.

• Journal of IKEEE
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• v.23 no.4
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• pp.1465-1468
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• 2019

The image noise improvement algorithm proposed in this paper extracts the boundary line by using the window of the binarized image to detect the gesture motion. Boundary line blurring is prevented by improving Gaussian noise generated during video output. To improve gesture recognition in low-light environments, an image noise enhancement algorithm has been designed to provide an output image close to the base image. Analyzing the experimental results, we found almost 10% improvement in the results compared to the results of the existing Median filter.

• Journal of The Korean Astronomical Society
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• v.37 no.5
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• pp.605-609
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• 2004

We present a set of high-resolution 3D MHD simulations exploring the evolution of light, supersonic jets in cluster environments. We model sets of high- and low-Mach jets entering both uniform surroundings and King-type atmospheres and propagating distances more than 100 times the initial jet radius. Through complimentary analyses of synthetic observations and energy flow, we explore the detailed interactions between these jets and their environments. We find that jet cocoon morphology is strongly influenced by the structure of the ambient medium. Jets moving into uniform atmospheres have more pronounced backflow than their non-uniform counterparts, and this difference is clearly reflected by morphological differences in the synthetic observations. Additionally, synthetic observations illustrate differences in the appearances of terminal hotspots and the x-ray and radio correlations between the high- and low-Mach runs. Exploration of energy flow in these systems illustrates the general conversion of kinetic to thermal and magnetic energy in all of our simulations. Specifically, we examine conversion of energy type and the spatial transport of energy to the ambient medium. Determination of the evolution of the energy distribution in these objects will enhance our understanding of the role of AGN feedback in cluster environments.

• Journal of The Korean Astronomical Society
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• v.52 no.5
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• pp.181-205
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• 2019

There is evidence that the luminosities of Type Ia supernova (SN Ia) depend on their environments. While the impact of this trend on estimating cosmological parameters is widely acknowledged, the origin of this correlation is still under debate. In order to explore this problem, we first construct the YONSEI (YOnsei Nearby Supernova Evolution Investigation) SN catalog. The catalog consists of 1231 spectroscopically confirmed SNe Ia over a wide redshift range (0.01 < z < 1.37) from various SN surveys and includes light-curve fit data from two independent light-curve fitters, SALT2 and MLCS2k2. For a sample of 674 host galaxies, we use the stellar mass and the star formation rate data in Kim et al. (2018). We find that SNe Ia in low-mass and star-forming host galaxies are $0.062{\pm}0.009mag$ and $0.057{\pm}0.010mag$ fainter than those in high-mass and passive hosts, after light-curve corrections with SALT2 and MLCS2k2, respectively. When only local environments of SNe Ia (e.g., locally star-forming and locally passive) are considered, this luminosity difference increases to $0.081{\pm}0.018mag$ for SALT2 and $0.072{\pm}0.018mag$ for MLCS2k2. Considering the significant difference in the mean stellar population age between the two environments, this result suggests that the luminosity evolution of SNe Ia with redshift is most likely the origin of the environmental dependence.

• Journal of the Speleological Society of Korea
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• no.85
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• pp.21-24
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• 2008

The chlorophyta and thebryophyta are became extinct by the shutting out the light and low temperature in caves. Whenever they get the conditions, they grow again immediately. It is necessary to keep the illumination distance over 2m and use the indirect light. The effect of lamp light and temperature is very important in the control of green contamination but the water and moisture in caves are essential factors in green contamination in the show caves. It's better to get rid of green alae and mosses at early stage for the control of the increase of green contamination. They must be isolated completely without the dispersion with moist pieces of cloth or sponge. It is necessary to shut out the cave route periodically for the restoration of cave environments and ecosystem. It's better to use the lamp keeping illumination and restricting the ascension of heat for the control of green contamination.