• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.475-481
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• 2014

As the society develops to advanced digital information times, many studies are underway about digital video processing technology areas such as image restoration. There are typical methods to restore the image which have been damaged by the impulse noise like SM(standard median) filter and CWM(center weighted median) filter. These filters show excellent noise reduction capabilities in low noise density areas, but in high noise density areas, noise reduction capabilities are not sufficient. In this paper, in order to restore the degraded images in impulse(Salt & Pepper) noise environment, the image restoration filter algorithm was suggested which expands and subdivide the mask focusing on damaged pixels. And to demonstrate the superiority of the proposed algorithm used PSNR (peak signal to noise ratio) as the standard of judgement.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.175-182
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• 2017

Noises are usually generated by various external causes and low quality devices in image data acquisition and recording as well as by channel interference in image transmission. Since these noise signals result in the loss of information, subsequent image processing is subject to the corruption of the original image. In general, image processing is performed in the mixed noise environment where common types of noise, known to be Gaussian and impulse, are present. This study proposes an iterative weighted mean filter for reducing mixed type of noise. Impulse noise pixels are first turned off in the input image, then $3{\times}3$ sliding window regions are processed by replacing center pixel with the result of weighted mean mask operation. This filtering processes are iterated until all the impulse noise pixels are replaced. Applied to images corrupted by Gaussian noise with ${\sigma}=10$ and different levels of impulse noise, the proposed filtering method improved the PSNR by up to 12.98 dB, 1.97 dB, 1.97 dB respectively, compared to SAWF, AWMF, MMF when impulse noise desities are less than 60%.

• Journal of Broadcast Engineering
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• v.16 no.5
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• pp.873-882
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• 2011

In this paper, we propose an efficient deinterlacing algorithm which interpolates the missing scan lines by weighted summing of the intra and the inter interpolation pixels according to the spatio-temporal variation. In the spatial interpolation, we adopt a new edge based spatial interpolation method which includes edge directional refinement. The conventional edge dependent interpolation algorithms are very sensitive to noise due to the failure of estimating edge direction. In order to exactly detect edge direction, our method first finds the edge directions around the pixel to be interpolated and then refines edge direction of the pixel using weighted maximun frequent filter. Futhermore, we improve the accuracy of motion detection by reducing the possibility of motion detection error using 3 tab median filter. In the final interpolation step, we adopt weighted sum of intra and inter interpolation pixels according to spatio-temporal variation ratio, thereby improving the quality in slow moving area. Simulation results show the efficacy of the proposed method with significant improvement over the previous methods in terms of the objective PSNR quality as well as the subjective image quality.

• Annals of Clinical Neurophysiology
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• v.4 no.2
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• pp.114-118
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• 2002

Backgrounds : Carpal tunnel syndrome (CTS) is a common condition that is usually diagnosed by electrophysiologic studies. However, CTS provide limited information to determine the causes of CTS and to choose the treatment method. We evaluated diagnostic sensitivity of MR imaging and treatment decisions by MR imaging in electrodiagnosed CTS. Methods : 14 patients (26 wrists) with electrodiagnosed CTS were studied using MR imaging. In 26 wrists for which axial T1 & T2 weighted images were obtained at 1.5T with a decided wrist coil. Previously described MR imaging of CTS such as increased median nerve signal, flattening of median nerve, reticular bowing, tenosynovitis and space occupying lesions were retrospectively evaluated. Degree of improvement was evaluated by global symptom score (GSS). The GSS rated symptoms from 0 (no symptoms) to 10 (severe) in each of five categories: pain, numbness, paresthesia, weakness/clumsiness, and nocturnal awakening. Subjects' GSS was recorded at baseline, 2 weeks, 1 month, 6 months after treatment. We decided to medical treatment that showed mainly inflammatory sign such as increased median nerve signal, tenosinovitis and to surgical treatment such as space occupying lesion, high canal pressure sign. Results : MR imaging showed that increased median nerve signal were in 20 wrists (77%), flattening of median nerve were in 6 wrists (23%), reticular bowing were in 3 wrists (12%), tenosynovitis were in 8 wrists (32%), decreased canal size in 2 wrists (7.6%), space occupying lesion were in 1 wrist (4%). A good outcome was revealed in 21 wrists by medical treatment that showed mainly increased median nerve signal, tenosynovitis. The mean GSS were 27.7 at baseline, 11.2 at 2 weeks, 11.0 at 6 months in medical treatment group. Another 5 wrist had surgical treatment shown by ganglion and high canal pressure sign such as median nerve flattening, reticular bowing, decreased canal size: 3 wrists had good prognosis, but 2 wrists (one patient) had no significant improvement due to small carpal tunnel size. Conclusions : Our results are in agreement with most previously described MR imaging signs of CTS. MR imaging plays an important role in several cases and especially in the assessment of failure of surgical treatment. Knowledge of MR findings may permit more rational choice of treatment.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.676-678
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• 2016

Noise occurs in the process of obtaining, transmission, and processing in image processing systems and these noises lower the resolution of image and cause visual errors. Currently, a representative filter to remove salt and pepper noise is SMF(standard median filter) but it is inadequate in preserving edge as the noise field expands. Therefore, for salt and pepper noise enviroment, the study suggested and algorithm that are places with original pixel when the central pixel is non-noise and process by applying weighted value on areas with less local mask pixel changes when there is noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.118-120
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• 2021

In modern society, with the development of IoT technology, various digital equipment is being distributed in a wide range of fields such as CCTV and exploration robots. Accordingly, the importance of data processing is increasing, and various studies are being conducted to remove noise generated in the process of receiving data in the imaging field. Representative noise includes additive white Gaussian noise (AWGN), and existing filters for removing noise include an average filter (AF), an alpha trimmed average filter (A-TAF), and a median filter (MF). However, existing filters have a disadvantage in that they show somewhat insufficient performance in noise removal characteristics in high frequency areas. Therefore, in this paper, in order to effectively remove AWGN existing in the high frequency region, a weight filter according to a distance based on the standard deviation is proposed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1788-1792
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• 2006

The ordered weighted averaging (OWA) operator by Yager has received more and more attention since its appearance. One key point in the OWA operator is to determine its associated weights. Among numerous methods that have appeared in the literature, we notice the maximum entropy OWA (MEOWA) weights that are determined by taking into account two appealing measures characterizing the OWA weights. Instead of maximizing the entropy in the formulation for determining the MEOWA weights, the new method in the article tries to obtain the OWA weights which are evenly spread out around equal weights as much as possible while strictly satisfying the orness value provided in the program. This consideration leads to the least squared OWA (LSOWA) weighting method in which the program tries to obtain the weights that minimize the sum of deviations from the equal weights since entropy is maximized when the weights are equal. Above all, the LSOWA weights display symmetric allocations of weights on the basis of equal weights. The positive or negative allocations of weights from the median as a basis depend on the magnitude of orness specified. Further interval LSOWA weights are constructed when a decision-maker specifies his or her value of orness in uncertain numerical bounds.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.128-132
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• 1996

In [1], it is shown that a subclass of ETBFs, which are self-dual ETBFs, can be expressed as a weighted average of median subfiltered outputs. In this paper, we extend this result to general ETBFs. In particular, we show that any ETBF can be represented as a weighted average of minimum (or maximum) subfiltered outputs. These representations naturally lead to a subclass of ETBF, called the K-th order ETBF (K-ETBF) that employs only those subfilters whose window sizes are less than or equal to K. By designing K-ETBFs under the mean square error criterion for various values of K and applying them to restore noisy signals, the tradeoff between the performance and the complexity of this class of filters is examined.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3873-3887
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• 2018

In this paper, we propose a novel method to recover images corrupted by impulse noise. The proposed method uses two stages: noise detection and filtering. In the first stage, we use pixel values, rank-ordered logarithmic difference values, and median values to train a neural-network-based impulse noise detector. After training, we apply the network to detect noisy pixels in images. In the next stage, we use group-based weighted couple sparse representation to filter the noisy pixels. During this second stage, conventional methods generally use only clean pixels to recover corrupted pixels, which can yield unsuccessful dictionary learning if the noise density is high and the number of useful clean pixels is inadequate. Therefore, we use reconstructed pixels to balance the deficiency. Experimental results show that the proposed noise detector has better performance than the conventional noise detectors. Also, with the information of noisy pixel location, the proposed impulse-noise removal method performs better than the conventional methods, through the recovered images resulting in better quality.