• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.1-8
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• 2005

There are many image segmentation methods having bon published as the results of research so far, however these are for the noise images which can process an image under the general white noise environments. Therefore, these methods has the disadvantages because it is difficult to extract only the accurate parameters, which can distinguish between image and impulsive noise, from image with impulsive noises. So it has a problem about the potential decreasing of the performance according to the impulsive noise for all applications using the present quadtree segmentation. In this paper, we propose new quadtree segmentation using quartile deviation which can extract effectively the image information parameters from a noise image. Therefore our method can apply for various image processing fields because it has a advantage to distinguish an image information from noise image. As the result of simulation, we confirm that the proposed quadtree segmentation is more efficient than the present method when tested on impulsive noise image.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1462-1467
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• 2013

A pseudo 480-Hz drive method has been proposed to reduce the dynamic false contour noise that occurs on flat panel displays with displaying grayscale image in the digital mode, such as plasma display panels. The proposed method makes the image movements nearly continuous by rearranging the 8-bit image data displayed for 1 TV field into 8 subfields. The position of the image data rearranged in subfields has been optimized on the basis of the speed of the moving image by computer simulations for the dynamic false contour noise. It is verified that a significant reduction in the dynamic false contour noise is achieved with the proposed method as compared to the conventional noise reduction technologies. Moreover, to reduce the noise in digital mode displays, the proposed technology requires only 8 subfields. Therefore, there is no reduction in the brightness of the image.

• ETRI Journal
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• v.34 no.4
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• pp.564-571
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• 2012

This paper presents an algorithm for removing high-density impulsive noise that generates some serious distortions in edge regions of an image. Although many works have been presented to reduce edge distortions, these existing methods cannot sufficiently restore distorted edges in images with large amounts of impulsive noise. To solve this problem, this paper proposes a method using connected lines extracted from a binarized image, which segments an image into uniform and edge regions. For uniform regions, the existing simple adaptive median filter is applied to remove impulsive noise, and, for edge regions, a prediction filter and a line-weighted median filter using the connected lines are proposed. Simulation results show that the proposed method provides much better performance in restoring distorted edges than existing methods provide. When noise content is more than 20 percent, existing algorithms result in severe edge distortions, while the proposed algorithm can reconstruct edge regions similar to those of the original image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.54-61
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• 2023

With the development of IoT technologies and artificial intelligent, diverse digital image equipments are being used in industrial sites. Because image data can be easily damaged by noise while it's obtained with a camera or a sensor and the damaged image has a bad effect on the process of image processing, noise removal is being demanded as preprocessing. In this thesis, for the restoration of image damaged by the noise of random impulse, a switching filter algorithm based on noise estimation was suggested. With the proposed algorithm, noise estimation and error distraction were carried out according to the similarity of the pixel values in the local mask of the image, and a filter was chosen and switched depending on the ratio of noise existing in the local mask. Simulations were conducted to analyze the noise removal performance of the proposed algorithm, and as a result of magnified image and PSNR comparison, it showed superior performance compared to the existing method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.166-173
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• 2005

Preserving image features, edges and details in the process of impulsive noise filtering is an important problem. To avoid image blurring, only corrupted pixels must be filtered. In this paper, we propose an effective impulse noise detection method using Self-Organizing Neural Network(SONN) which applies median filter selectively for removing random-valued impulse noises while preserving image features, edges and details. Using a $3\times3$ window, we obtain useful local features with which impulse noise patterns are classified. SONN is trained with sample image patterns and each pixel pattern is classified by its local information in the image. The results of the experiments with various images which are the noise range of $5-15\%$ show that our method performs better than other methods which use multiple threshold values for impulse noise detection.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.667-670
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• 2013

Noise is unwanted in high quality images, but it can aid image tampering. For example, noise can be intentionally added in image to conceal tampered regions or to create special visual effects. It may also be introduced unknowingly during camera imaging process, which makes the noise levels inconsistent in splicing images. In this paper, we present an image forgery detection method using a noise dependent watershed transformation. Image is segmented into objects for initial noise estimation by the watershed transformation, and different noise level in objects are estimated to obtain final decision result. Experimental results of the proposed method on natural images are presented.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.11-19
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• 2023

In this paper, it is confirmed that the metrics used to evaluate image quality can be applied to low-light images. Due to the nature of low-illumination images, factors related to light create various noise patterns, and the smaller the amount of light, the more severe the noise. Therefore, in situations where it is difficult to obtain a clean image without noise, the quality of a low-illuminance image from which noise has been removed is often judged by the human eye. In this paper, noise in low-illuminance images for which ground truth cannot be obtained is removed using Noise2Noise, and spatial resolution and radial resolution are evaluated using ISO 12233 charts and colorchecker as metrics such as MTF and SNR. It can be shown that the quality of the low-illuminance image, which has been evaluated mainly for qualitative evaluation, can also be evaluated quantitatively.

• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.923-936
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• 2014

Images can be contaminated with different types of noise, for different reasons. The neighborhood averaging and smoothing by image averaging are the classical image processing techniques for noise removal. The classical spatial filtering refers to the aggregate of pixels composing an image and operating directly on these pixels. To reduce or remove effectively noise in image sequences, it usually needs to use noise reduction filter based on space or time domain such as method of spatial or temporal filter. However, the method of spatial filter can generally cause that signals of objects as the target are also blurred. In this paper, we propose temporal filter using the piece-wise quadratic function model and enhancement algorithm of image quality for the performance improvement of target detection and tracking by heterogeneous noise reduction. Image tracking simulation that utilizes real IIR(Imaging Infra-Red) images is employed to evaluate the performance of the proposed image processing algorithm.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.32-34
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• 2007

The X-ray projection system has long been used for steel-tube inspection and weld monitoring. The thickness of tubes and welded areas is based on the evaluation of radiographic shadow projections. The traditional tangential measurement estimates the distance of border lines of the projected wall shadows of a tube onto a radiographic image detector. The detected image in which although there is a variety of noise may be sectioned into several partitions according to its specific blocks. Imaging noise originates from most of elements of the system, such as shielding CCD camera, imaging screen, X-ray source, inspected object, electronic circuits and etc. The tangential projection incorrectness and noise influence on imaging quality. In this paper we first sectionalize the X-ray image on the basis of vertical contrast difference. And next functional and statistic analysis are carried on at each region. Geometrical distance and unsharpness of the edge caused by visual evaluation uncertainties are also discussed.