• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.647-653
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• 2016

Recently, the data image processing has been applied to a variety of fields including broadcasting, communication, computer graphics, medicine, and so on. Generally, the image data may develop the noise during their transmission. Therefore, the studies have been actively conducted to remove the noise on the image. 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 histogram and spatial weighted values 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. Regarding the processed results by applying Lena images which were corrupted by salt and pepper noise(P=50%) and AWGN(${\sigma}=10$), suggested algorithm showed the improvement by 7.06[dB], 10.90[dB], 5.97[dB] respectively compared with the existing CWMF, A-TMF, AWMF.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.463-466
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• 2008

With the development of Information Technology in recent years, the image has been an important means to store or express information. Generally, during the process of acquiring and storing images, the images can be corrupted by noise of which typical types are Impulse(Impulse Noise) and AWGN(Addiction White Gaussian Noise). Impulse noise shows irregularly in black and white over the length and breadth of the image by sharp and sudden disturbance of the image signal. In the Impulse noise environment, SM(Standard Median) filter would be used because of its good noise removal performance and simple algorithm. However, when SM filter removes noise, it also produces error at the edge of image and causes whole image quality deterioration. In this paper, we propose a method based on modified nonlinear filter operation scheme which enhances the features of noise removal and detail image preservation when restoring image in Impulse noise environment. And, we compared it with existing methods and the performances through simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.394-400
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• 2016

Currently, with the rapid development of the digital age, multimedia-related image devices become popular. However image deterioration is generated by multiple causes during the transmission process, with typical example of salt and pepper noise. When the noise of high density is added, existing methods are deteriorated in the characteristics of removal noise. After judging the noise condition to remove the salt and pepper noise, if the center pixel is the non-noise pixel, it is replaced with the original pixel. On the other hand, if it is the noise pixel, algorithm is suggested by the study, where the histogram of the corrupted image and the median filters are used. And for objective judgment, the proposed algorithm was compared with existing methods and PSNR(peak signal to noise ratio) was used as judgment standard. As the result of the simulation, The proposed algorithm shows a high PSNR of 32.57[dB] for Lena images that had been damaged of a high density salt and pepper noise(P=60%), Compared to the existing CWMF, A-TMF and AWMF there were improvements by 21.67[dB], 18.07[dB], and 20.13[dB], respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.113-120
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• 2020

With the recent development of communication equipment, the demand for communication equipment is gradually increasing. Accordingly, various signal processing has been studied. In the case of an image, noise removal is an indispensable step because noise propagation problems may occur if noise is not removed in the pre-processing process. Salt and Pepper noise is a typical impulse noise with two extremes. Various studies have been conducted to remove such noise, and there are CWMF, MF and MMF. However, the existing methods are somewhat insufficient in the high-density noise region. Therefore, in this study, we have proposed an algorithm that filters the size of the mask according to the number of noises inside the 7×7 mask and filters it with a modified switching filter using the histogram distribution of the image. In the case of the proposed algorithm, noise can be effectively removed in a high-density noise region. For objective judgment, PSNR was used to compare and analyze with existing algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1255-1261
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• 2020

Currently, the spread of mobile devices is gradually increasing. Accordingly, various techniques using images or photos are actively being researched. However, image data generates noise for complex reasons, and the accuracy of image processing increases according to the performance of removing noise. Therefore, noise reduction is one of the essential steps. Salt and pepper noise is a typical impulse noise in the image, and various studies are being conducted to remove the noise. However, existing algorithms have poor noise rejection performance in high frequency areas, and average filters have blurring. Therefore, in this paper, we propose an algorithm that effectively removes salt and pepper noise in the high frequency region as well as the low frequency region using entropy. For objective and accurate judgment of proposed algorithms, MSE and PSNR were used to compare and analyze existing algorithms.

• Particle and aerosol research
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• v.13 no.1
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• pp.17-23
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• 2017

In this study, the test results obtained from the performance tests for CA (Korea Association of Cleaning Air) certificated air cleaners which had been commercially available in Korea from 2003 to 2015 were analyzed. Among the test parameters such as flow rate, particle collection efficiency, clean air delivery rate (CADR), ozone emission, odor removal efficiency and noise level, noise level and CADR were correlated with flow rates. Collection and odor removal efficiencies were 20% higher than the limit of the CA certification. The ozone emissions from the air cleaners were negligible because all the air cleaners were equipped with only HEPA filters, not electrostatic precipitation method which produces ozone.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1741-1744
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• 2005

The electron beam machining provides very high resolution up to nanometer scale, hence the E-Beam writing technology is rapidly growing in MEMS and nano-engineering areas. For E-Beam machining, $2^{nd}$ electron detector is required to see a machined sample at the stage. The $2^{nd}$ electron detector is composed of scintillator and photomultiplier with signal amplifier and high voltage power supplier. Since a photomultiplier tube is an extremely high-sensitivity photodetector, the signal light level to be detected is very low and therefore particular care must be exercised in shielding external light. In this paper, the design methodology of $2^{nd}$ electron detector and the image noise removal method are introduced.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2029-2036
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• 2017

Currently, the demand for multimedia services is increasing with the rapid development of the digital age. Image data is corrupted by various noises and typical noise is mainly AWGN, salt and pepper noise and the complex noise that these two noises are mixed. Therefore, in this paper, the noise is processed by classifying AWGN and salt and pepper noise through noise judgment. In the case of AWGN, the outputs of spatial weighted filter and pixel change weighted filter are composed and processed, and the composite weights are applied differently according to the standard deviation of the local mask. In the case of salt and pepper noise, cubic spline interpolation and local histogram weighted filters are composed and processed. This study suggested the multiple image restoration filter algorithm which is processed by applying different composite weights according to the salt and pepper noise density of the local mask.

• 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.