• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.121-123
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• 2021

Image processing is being used in various forms in important fields of the 4th industrial revolution, such as artificial intelligence, smart factories, and the IoT industry. In particular, in systems that require data processing such as object tracking, medical images, and object recognition, noise removal is used as a preprocessing step, but the existing algorithm has a drawback in that blurring occurs in the filtering process. Therefore, in this paper, we propose a filter algorithm using switching fuzzy weights. The proposed algorithm switches the fuzzy function by dividing the low-frequency region and the high-frequency region by the standard deviation of the filtering mask, and obtains the final output according to the fuzzy weight. The proposed algorithm showed improved results compared to the existing method, and showed excellent characteristics in the region where the high-frequency component is strong.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1689-1696
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• 2017

In the process of obtaining and transmitting image, there is degradation of image due to various noise, and there have been many active studies ongoing to remove the noise added on the images. This thesis has proposed a switching filter processing by the types of noise in order to remove the complex noise added on images. When the center value of local mask is damaged by AWGN based on the noise judgment, the threshold value is applied on standard deviation of local mask to process by applying different weight of weighted mask, and if the image is damaged by the salt and pepper noise, the local mask is subdivided into 4 directions, and processed by applying cubic spine interpolation in the direction with the least slat and pepper noise. Also, in order to evaluate the performance of proposed filter algorithm, the study conducted comparison among the existing methods and proposed filter using PSNR.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.675-681
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• 2022

Recently, with the development of IoT technology and AI, unmanned and automated in various fields, interest in video processing, which is the basis for automation such as object recognition and object classification, is increasing. Various studies have been conducted on noise removal in the video processing process, which has a significant impact on image quality and system accuracy and reliability, but there is a problem that it is difficult to restore images for areas with high impulse noise density. In this paper proposes a filter algorithm based on partial mask and Lagrange interpolation to restore the damaged area of impulse noise in the image. In the proposed algorithm, the filtering process was switched by comparing the filtering mask with the noise estimate and the purge weight was calculated based on the low frequency component and the high frequency component of the image to restore the image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1215-1220
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• 2016

As society has developed rapidly toward a highly advanced digital information age, a multimedia communication service for acquisition, transmission and storage of image data as well as voice has being commercialized. However, image data is always corrupted by various noises during image processing, so researches for removing noises have been continued until now. There are diverse types of noise on the image including salt and pepper noise, AWGN, and mixed noise. Hence, the filter algorithm for the image recovery was proposed that salt and pepper noise was processed by linear interpolation, histogram weighted values and median filter after defining the noise to lessen the impact of mixed noise added in the image, and AWGN was processed by the pixel information of local mask establishing the weighted values in this study. In addition, the algorithm was compared with the conventional methods for objectively and used the PSNR(peak signal to noise ratio) as the basis of the determination.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1284-1289
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• 2016

Recently, digital image processing is applied a lot to the broadcasting, communication, computer graphic, and medical sectors. It generates noise when data is transmitted. There are many kinds of noises that add to the image such as salt and pepper noise, AWGN, and complex noise. Thus, this study divides the corrupted image into four4 areas and estimates the types of noises each pixel, and this study suggested a switching filter that separates the estimated into salt and pepper noise and AWGN. In the case that center pixel of local mask is corrupted by salt and pepper noise, it used a histogram probability weighting of subdivided area. Also, in case that it is corrupted by AWGN, algorithm that is applied to with different weights given for the distribution of each area with using subdivided area's distribution was suggested. For an objective comparison and conclusion, this study used PSNR and compared to existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1373-1379
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• 2007

A modified adaptive switching median filter for impulse noise removal, which has the noise detection step and the noise filtering step, is proposed in this paper. In the noise detection step, we use the detection threshold which is earned by calculating the intensity differences between pixels nearby with each other in localized window, to determine whether the pixels in the image are noise or not. Then in the noise filtering step, we will only remove the corrupted pixels and remain the good pixels. By the noise detection result, we can easily get the local noise density of the image, and use it to consider the filtering mask size and the times of filtering iteration according to different localized noise corruptions. For Setting the simulation result, we compared the proposed method to conventional median filters with several test images corrupted by various impulse noise densities. We also use the peak signal-to-noise ratio (PSNR) to evaluate restoration performance, the simulation results demonstrate that the proposed method shows better results than other median-based type filters.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.3 s.357
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• pp.92-97
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• 2007

Switching mode amplifiers have been studied widely for use at microwave frequency range, and the class E amplifier which is a type of switching mode amplifier offers very high efficiency approaching 100%. In this paper, 2.4GHz microwave class E amplifier with 66% power added efficiency (PAE) and 17.6dBm output has been linearized for use at wireless LAN transmitter, and digital predistortion technique with look up table is applied. With -3dBm input power of wireless LAN, measured output spectrum can meet the required IEEE 802.11g standard spectrum mask, and the digital predistortion output spectrum has been improved by 5dB of ACPR at 20MHz offset from center frequency.

• Proceedings of the KIEE Conference
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• 2006.10a
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• pp.99-100
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• 2006

절연 게이트 바이폴라 트랜지스터 (Insuialed atc Bipolar Transistor : IGBT)는 높은 전류구동 능력과 높은 입력 임피던스 특성으로 인해 대전력 스위칭 소자로 널리 응용되고 있다. 특히, 대용량 모터 구동을 위해 응용되는 경우, 모터의 부하 특성상, 모터의 단락에 의한 단락 회로 (Short-circuit fault) 현상을 비롯한 클램핑 다이오드의 파손으로 인한 unclamped 유도성 부하 스위칭 (UIS) 상황에서 견딜 수 있도록 설계되어야 한다. 이를 위해, 이전 연구를 통해 Floating p-well을 600V급 IGBT에 도입함으로써 UIS 상황에서 IGBT가 견딜 수 있는 에너지(항복 에너지)륵 증가시키고 Floating p-weil 전압을 감지함으로써 단락 회로 상황에서 IGBT가 보호될 수 있도록 보호회로를 제안하고 검증하였다. 그러나 이 보호회로는 수평형 금속 산화막 반도체 전계 효과 트랜지스터 (Latcral MOSFET)로 제작됨으로써 보호회로 기능을 수행하기 위해서는 넓은 면적을 요구하였다. 또한, 정상적인 동작 상황에서 오류를 감지 (오류 감지: False detection)하는 동작으로 인해 추가적인 filter를 요구함으로써 보호회로 동작 속도를 감소시켰다. 이러한 단점을 해결하기 위해, 수평형 절연 게이트 바이폴라 트랜지스터 (Lateral IGBT : LIGBT)를 보호회로에 적용함으로써 LIGBT의 높은 전류 구동능력을 이용하여 기존 보호회로 면적의 30% 수준의 보호회로를 구현하였다. 또한, 구현된 보호회로는 오류 감지 현상을 제거함으로써 보호회로의 동작 속도를 개선하였다. 제안된 보호회로와 1200V급 IGBT는 7장의 마스크를 이용한 표준 수평형 IGBT 공정을 이용하여 제작되었으며, 특히, 전자빔 조사를 이하여 턴오프 속도를 개선함으로써 고속 스위칭에 적합하도록 최적화 되었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.645-651
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• 2021

With the advent of the Fourth Industrial Revolution, modern society uses a diverse pool of devices. In this context, there is increasing interest in removing various kinds of noise arising in data transmission. However, it is difficult to restore image that damaged by mixed noise, and a digital filter that effectively restores an image according to the characteristics of the noise is required. In this paper, we propose a digital switching filter algorithm to remove mixed noise generated during digital image transmission. The proposed algorithm switches the filtering process through noise judgment and reconstructs the image using fuzzy weights and combined weights based on the pixel values inside the mask. To evaluate the proposed algorithm, we compared it with existing filter algorithms through simulation. Filtering results were expanded and compared for visual evaluation, and PSNR comparison was used for quantitative evaluation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.