• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.481-483
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• 2018

Recently, as the demand for electronic communication equipment increases, the importance of image and signal processing is increasing. However, noise is generated in digital signal due to various causes during transmission and reception, lowering equipment reliability and causing malfunction. Particularly, since AWGN may be found in most electronic equipments, AWGN removal is mandatorily performed as a preprocessing phase in various fields, such as image recognition, extraction, and segmentation. In the present paper, an AWGN removal algorithm which considers high frequency components is proposed. Conventional methods show relatively inadequate performance in images with high frequency components. To overcome this problem, proposed is a filter algorithm that add or subtract difference images in the local mask. And to verify performance of the proposed algorithm, PSNR and enlarged images are used to compare with the existing methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.524-527
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• 2005

A society has progressed rapidly toward the highly advanced digital information age. However, noise is generated by several causes, when signal is processed. Therefore, methods for eliminating those noises have researched. There were the existing FFT(fast fourier transform) and STFT(short time fourier transform) for removing noise but it's impossible to know information about time and time-frequency localization capabilities have conflictive relationship. Therefore, for overcoming these limits, wavelet-based denoising methods that are capable of multiresolution analysis are applied to the signal processing field. However, existing threshold- and correlation-based denoising methods consider only statistical characteristics for noise, accordingly a lot of noise is acceptable as an edge and are impossible to remove AWGN and impulse noise, at the same time. Hence, in this paper we proposed wavelet-based new denoising algorithm and compared existing methods with it.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.2
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• pp.71-76
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• 2016

Digital image processing is being utilized in various fields including medical industry, satellite photos, and factory automation image recognition. However, this kind of image data produces heat by an external cause in the course of being processed, transmitted, and stored. Most typical noises added in the images are AWGN and salt and pepper. MF, CWMF, and AWMF are methods used to restore images damaged by AWGN and the existing methods are likely to damage detailed information such as an edge. Therefore, this paper suggests an algorithm applying weight of average filter, average filter depending on pixel, and spatial weight filter based on edge size of local mask in an AWGN environment, in a different way. Also, this paper compares functions of existing methods by using PSNR to prove excellence of the suggested algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.125-131
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• 2019

Through the 4th Industrial Revolution, various digital equipments are being distributed, and accordingly, the importance of data processing is increasing. As data processing has a great effect on the reliability of equipment, its importance is increasing, and various studies are being conducted. In this paper, we propose an algorithm to remove AWGN in consideration of the noise in the image. The proposed algorithm is used in the filtering process by inferring the standard deviation of the image noise. The noise is removed by dividing the filter for the high frequency component and the filter for the low frequency component compared with the standard deviation of the filtering mask. The proposed algorithm is simulated with the existing methods for evaluation and compared and analyzed by difference image, PSNR and profile. The proposed algorithm minimizes the effect of noise and preserves the important characteristics of the image and shows the performance of efficient noise removal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.394-400
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• 2019

Noise removal is one of the required step in processing digital video and there are many researches to develop algorithm that fits with its purpose and environment. However, present impulse noise removal methods are lacking in its function in terms of removing noise in edge and high frequency factors. Therefore, this research has Extended range of masks depending on density to determine noise so that high frequency factors can be preserved. The range of resolution is set based on median and standard deviation of inside resolution after removing impulse noise. afterwards, those resolution within the range are calculated by adding weight to have the final output value. The suggested algorithm has an enhanced function in removing noise in various areas with many edge and high frequency factors than present methods and their functions are compared through simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.230-236
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• 2015

In these digital times, the image signal processing is being used in various areas like vehicle recognition, security, and robotics. Generally, the image deterioration occurs by salt & pepper noise in the procedures of image transmission, storage, and processing. Methods to remove this noise are SMF, CWMF, and SWMF and these methods have few unsatisfactory noise reduction characteristics in salt & pepper noise environment. Therefore, in order to mitigate salt & pepper noise which is added in the image, this study suggested an algorithm which subdivides the masks in the image into four areas and processes using non-noise pixel numbers in each area. Additionally, in order to prove the excellence of the proposed algorithm, relevant performances were compared with existing methods using PSNR.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.840-846
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• 2016

Recently, with the rapid demand expansion on the devices of digital image processing, the excellent quality of the images is required. However, the degradation of the images occurs in the processes of data acquisition, processing, and transmission by various external causes and the noise has been well known as the major cause of image degradation. There are a variety of noises to be added on the images, with typical example of AWGN. Hence, in this article, we suggested average filter algorithm processed by the threshold values using them applying standard deviation of local mask under the AWGN environment in this study. As the result of the simulation, The proposed algorithm shows a high PSNR of 24.56[dB] for Barbara images that had been damaged of AWGN(${\sigma}=15$), compared to the existing MF, CWMF and AWMF there were improvements by 3.34[dB], 2.57[dB], and 3.32[dB], respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.722-728
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• 2020

Digital image processing has been applied in a wide range of fields due to the development of IoT technology and plays an important role in data processing. Various techniques have been proposed to remove such noise, but the conventional impulse noise canceling methods are insufficient to remove noise of edge components of an image, and have a disadvantage of being greatly affected by random impulse noise. Therefore, in this paper, we propose an algorithm that effectively removes edge component noise in random impulse noise environment. The proposed algorithm calculates the threshold value by determining the noise level and switches the filtering process by comparing the reference value with the input pixel value. The proposed algorithm shows good performance in the existing method, and the simulation results show that the noise is effectively removed from the edge of the image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1342-1348
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• 2017

In an era where digital data takes on great importance, images are essential to various media. Noise is generated during the acquisition and transmission of such images, caused by a number of external factors. The removal of noise is an essential step in image processing. There are various methods used to remove noise, in accordance with the cause or form of the noise. AWGN is one of the leading methods. As such, this paper applies the edge detection method using the mean of each pixel after categorizing in detail the partial masks into nine areas as part of the preliminary process, in order to minimize noise that had been added to the image. In addition, the paper suggests an algorithm that applies different filters to the partial masks by using the critical mass value of the transfigured edge detection. To verify the competence of the suggested algorithm, it was compared with existing methods by using magnified images and PSNR(peak signal to noise ratio).

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.1
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• pp.28-36
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• 2008

This paper discusses theimplementation of the low-voltage, low-power, third-order, 1-bit switched capacitor delta-sigma modulator of the implantable cardiac pacemaker. The distributed, feed-forward structure and bulk-driven OTA were used in order to achieve an efficient operation under a supply voltage of 1V or lower. The designed modulator has a dynamic range of 49dB at 0.9V supply voltage and consumes 816nW of power. Such a significant reduction in power consumption allows diverse applications, not only in pacemakers, but also in implantable biomedical devices that operate with limited battery power. The core chip size of the modulator is $1000{\mu}m*500{\mu}m$ manufactured, with the $0.18{\mu}m$ CMOS standard process.