• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.965-972
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• 2015

Along with the rapid development in digital times, image media are being used in internet, computer and digital camera. But image deterioration occurs due to various exterior reasons in the procedures of acquisition, processing, transmission and recording of digital image and major reason is noise. Therefore in order to remove salt & pepper noise, this study suggested the algorithm which replaces the noise to original pixel in case of non-noise, and processes the noise with Lagrange interpolation method in case of noise. In case high density noise was added and the noise could not be removed, noise characteristics were improved by processing the noises repeatedly. And for objective judgment, this method was compared with existing methods and PSNR(peak signal to noise ratio) was used as judgment standard.

• Journal of Broadcast Engineering
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• v.6 no.2
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• pp.139-147
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• 2001

Since various noises degrade not only image quality but also compression efficiency in MPEG and H.263, pre-processing is necessary to reduce spatial and temporal noise and to increase ceding efficiency as well. In this paper, we propose a simplified method for noise detection, spatial and temporal noise reduction. Noise detection is based on correlation of the current pixel with its neighboring 4 pixels. Spatial noose reduction utilizes a non-rectangular median filter that is less complex than the conventional rectangular median filter. The proposed temporal filter is an IIR average filter using LUT(Look-up Table) to enhance subjective video quality. The proposed pre-processing method is very simple and efficient.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.300-303
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• 2015

Digital imaging process has been put to practical use in various application sectors due to a rapid advancement such as memory devices, etc. However, noises are being generated due to various reasons during the image processing and a variety of methods are being studied in order to eliminate these noises. Generally, images are damaged due to a mixed noise having different characteristics. In this paper, a filter algorithm which switches according to the noise types was proposed in order to mitigate the influence of mixed noise included in the image. And using the PSNR as the standard for objective decision making of the improvement effect, it was compared with the existing methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.798-801
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• 2014

Nowadays, the demand for multimedia services has grown with the rapid evolution in the digital era. But due to external causes in the process of processing, transmitting and storing image data, the images are damaged. One of the major causes of such damage is known to be noise. Some of the most commonly used methods for removing noise are CWMF(center weighted median filter), A-TMF(alpha-trimmed mean filter) and AWMF(adaptive weighted median filter). However, these filters all leave a bit to be desired in removing noise in a complex noise environment. Therefore this paper suggest an image restoration filter algorithm that first judges the noise and sets a adjustment weight based on the median value and distance of the mask to remove the complex noise. For an objective analysis, the results were compared against existing methods and the PSNR(peak signal to noise ratio) was used as a reference.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.253-255
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• 2022

Thanks to the 4th industrial revolution and the development of various communication media, technologies such as artificial intelligence and automation are being grafted into industrial sites in various fields, and accordingly, the importance of data processing is increasing. Image noise removal is a pre-processing process for image processing, and is mainly used in fields requiring high-level image processing technology. Various studies have been conducted to remove noise, but various problems arise in the process of noise removal, such as image detail preservation, texture restoration, and noise removal in a special area. In this paper, we propose a switching mask filter based on the noise intensity to preserve the detailed image information during the impulse noise removal process. The proposed filter algorithm obtains the final output by switching to the extended mask when it is determined that the density is higher than the reference value when noise is determined in the area designated as the filtering mask. Simulation was conducted to evaluate the performance of the proposed algorithm, and the performance was analyzed compared to the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.727-734
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• 2014

Digital processing technologies are being studied in various areas of image compression, recognition and recovery. However, image deterioration still occurs due to the noises in the process of image acquisition, storage and transmission. Generally in the typical noises which are included in the images, there are Gaussian noise and the mixed noise where the Gaussian noise and impulse noise are overlapped and in order to remove these noises, various researches are being executed. In order to preserve the edge and effectively remove mixed noises, image recovery filter algorithm was suggested in this study which sets and processes the adaptive weight using the median values and average values after noise judgment. Additionally, existing methods were compared through simulations and PSNR(peak signal to noise ratio) was used as a judgment standard.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.779-781
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• 2013

As the demand for various multimedia service increases the technology that utilizes image as information transfer method develops rapidly. Though average filter, median filter and weight filter etc. have been proposed to remove various noises that are added to images, the existing methods are short of noise removal and edge reservation performance. Therefore, in this paper an algorithm, in which noise is decided at the first hand, and then it is processed through modified median filter and adaptive weighted average filter, is proposed to effectively remove the complex noise that has been added to an image. And it was compared with existing methods through simulation and PSNR(peak signal to noise ratio) has been used as a criterion.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.33-38
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• 2005

Degradation is generated by several causes in the process of digitalization or transmission of data. And its essential cause is noise. Therefore, researches for wavelet-based methods which reconstruct signal degraded by noise have continued. In AWGN(addtive white gaussian noise) environment, the general trend for denoising is to use the thresholding method. Reconstructed signal includes a lot of noise because these methods only consider statistical characteristic regarding noise. In this paper, we present a new method which uses the cumulation of wavelet detail coefficients. As a result, reconstruction of edges and denoising performance are improved. Also we compare existing methods using SNR(signal-to-noise ratio) as the standard of judgement of improvemental effect.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.422-424
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• 2014

IPS 측위는 실내의 특정 위치의 무선 신호 정보를 수집하여 radio map을 구성하고 측위 시점에 구성된 정보와 수집된 무선 정보를 비교하여 현재 위치를 판단한다. 비교방법은 핑거프린트, 삼각 측량법 등의 알고리즘을 활용하며 최근에는 wifi 신호와 다른 신호를 혼합하여 계산하거나 다중 비교 방식을 사용하여 정확도를 높이는 측위 방법도 개발되었다. 앞서 언급한 측위방법은 비교적 정확한 실내 위치를 판단하지만 구축된 radio map정보와 측위 시점에서 수집된 신호 정보가 상이할 경우 측위 정확도는 크게 하락한다. 구축 시점과 측위 시점의 무선 신호에 차이가 발생하는 이유는 신호 발생 기기의 고장, 장애물 발생, 채널 간섭 현상 등이다. 특히 채널 간섭은 구축된 신호 영역 내에 동일 채널 및 인접 채널을 가지는 AP 및 테더링 기기 등이 신규로 설치되었을 때 측위에 영향을 주는 신호잡음 현상이다. 본 논문에서는 앞서 언급한 문제점을 해결하기 위해 신호 잡음에 강인한 IPS용 라디오맵에 대하여 제안한다. 제안하는 radio map 설계 방법은 측위 시점에 신호잡음이 발생하였을 때 이 정보를 수집하여 기존 radio map에 반영하여 정확도를 유지하는 방법이다. 이 방법을 적용할 경우 신호잡음에 능동적으로 대처하여 IPS 측위의 정확도를 높일 수 있을 것으로 사료된다.