• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.268-270
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• 2013

According to the development and supply of Wibro technology digital technology is applied in several fields. Digital images are damaged by various noises in the process of transfer and storage; the image restoration is to reduce the influence of the noises on images by removing the noises. To make good image restoration several methods have been proposed but the noise removal property is not satisfactory. Therefore, to effectively remove noises noise decision is made and if it is decided as a noise, the size of mask is enlarged; this is adaptive median filter algorithm that is proposed in this paper. And through simulation the superiority of this algorithm to existing methods has been verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.798-800
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• 2014

In recent years, according to the improvement of Digital image technology have been recently developed most of communication technology from multimedia communication service as well as image data transmission. But In the process of storing and transmitting noise is still generated in noise and the image degrades rapidly quality of a lot of image impulse noise. To eliminate this noise, SMF, CWMF, SWMF etc. The filters have been proposed to interfere with the noise characteristics of the filter are somewhat sufficient. Therefore, in this paper, in order to remove impulse noise is proposed a modified median filter. And impulse noise removal algorithms to confirm the existed PSNR(peak signal to noise ratio) from using conventional methods were compared.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.649-651
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• 2016

본 연구에서는 디지털 신호에 포함되어 있는 잡음을 효과적으로 제거하기 위한 방법으로 프로그램 가능한 디지털 FIR 필터를 제안한다. 이러한 필터는 Altera의 FPGA(Field Programmable Gate Array)인 cyclone II EP2C70F89618를 이용하여 설계하고 구현하였다. 데이터 신호 잡음 제거 알고리즘을 바탕으로 한 영상 신호 제거 결과는 출력 영상으로부터 알 수 있듯이 필터 적용 후 출력 영상은 적용 전의 출력 영상에 비해 월등히 구분이 가능한 출력 영상 특성을 보임을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.457-459
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• 2018

화자인식 기술은 주어진 임의의 두 발화로부터 발화자의 일치 여부를 판단하여 등록된 화자의 목록으로부터 임의로 입력된 발화의 발화자를 식별하는 기술이다. 그러나, 배경잡음이나 반향이 존재하는 경우에는 음성신호가 왜곡되어 화자인식 성능이 저하될 수 있기 때문에 별도의 음성신호 전처리 알고리즘을 함께 사용할 수 있다. 본 논문에서는 배경잡음이 존재하는 환경에서 다수의 마이크로폰을 통해 수집한 음성신호에 대해 화자인식을 수행하는 방법으로써 parametric multi-channel Wiener filter (PMWF)를 이용한 화자일치 점수 앙상블 기법을 제안한다. 입력신호의 신호대잡음비를 기준으로 점수 결합 시 사용되는 결합계수를 정하고, Wiener filter 로 잡음을 제거하여 얻은 점수와 minimum variance distortionless response (MVDR) 빔포머를 통해 잡음을 제거하여 얻은 정수를 가중결합하는 방식으로 동일오류율을 측정한 결과, 각 전처리 알고리즘을 독립적으로 사용하여 점수를 계산한 경우보다 우수한 성능을 보임을 확인할 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.9-15
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• 2009

This paper proposes a cancellation algorithm of impulse noise using a rank-order method. The proposed method is a fast and simple algorithm that is composed of two parts. The first part involves noise detection using a fuzzy technique, where an image is divided into RGB color channels. Then every pixel in each color channel is investigated and assigned a probability indicating its chances of being a noise pixel. At this time, the rank order method using a noise-detection mask is utilized for accurate noise detection. Thereafter, the second part involves noise-cancellation, where each noise-pixel value in an image is replaced in proportion to its fuzzy probability. Through the experiments, both the conventional and proposed methods were simulated and compared. As a result, it is shown that proposed method is able to detect noisy pixels more accurately, and produce resulting images with high PSNR values.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1076-1081
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• 2019

In response to the recent development of IT technologies, there are more demands for visual devices such as display. However, noise is generated in the process of sending video data due to various reasons. Noise is the representative noise which is commonly found. While A-TMF, CWMF, and AMF are the typical ways for removing Salt and Pepper noise, the noise is not removed well in high-density noise environment. To remove the noise in the high-density noise environment, this study suggested an algorithm which identifies whether it's noise or not. If it's not a noise, matches the original pixel. If it's a noise, divide the $3{\times}3$ local mask into the area of the element treated and the area of the element to be processed. Then, algorithm proposes to apply different weights for each element to treat it as an average filter. To analyze the performance of the algorithm, this study compared PSNR to compare the algorithm with other existing methods.

• 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 Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.251-256
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• 2021

Unlike general cameras, a high-speed camera capable of capturing a large number of frames per second can enable the advancement of some image processing technologies that have been limited so far. This paper proposes a method of removing undesirable noise from an high-speed input color image, and then detecting a human face from the noise-free image. In this paper, noise pixels included in the ultrafast input image are first removed by applying a bidirectional filter. Then, using RetinaFace, a region representing the person's personal information is robustly detected from the image where noise was removed. The experimental results show that the described algorithm removes noise from the input image and then robustly detects a human face using the generated model. The model-based face-detection method presented in this paper is expected to be used as basic technology for many practical application fields related to image processing and pattern recognition, such as indoor and outdoor building monitoring, door opening and closing management, and mobile biometric authentication.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.173-175
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• 2017

With the rapid progress of the digital area has come the increase in demand for multi-media services. Imaging processing as a result is being hailed as a technological field that can offer smart and efficient methods for the processing and analysis of images. In general, noise exist in various types, depending on the cause and form. Some leading examples of noise are AWGN(additive white Gaussian noise), salt and pepper noise and complex noise. This study suggests an algorithm to remove complex noise by using the standard deviation and noise density of the partial mask in order to effectively remove complex noise in images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.672-678
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• 2020

Ultra-high-speed images that accurately depict the minute movements of objects have become common as low-cost and high-performance cameras that can film at high speeds have emerged. In this paper, the proposed method removes unexpected noise contained in images after input at high speed, and then extracts an area of interest that can represent personal information, such as skin areas, from the image in which noise has been removed. In this paper, noise generated by abnormal electrical signals is removed by applying bilateral filters. A color model created through pre-learning is then used to extract the area of interest that represents the personal information contained within the image. Experimental results show that the introduced algorithms remove noise from high-speed images and then extract the area of interest robustly. The approach presented in this paper is expected to be useful in various applications related to computer vision, such as image preprocessing, noise elimination, tracking and monitoring of target areas, etc.