• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.763-768
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• 2011

Image denoising is a lively research field. Today the researches are focus on the wavelet domain especially using wavelet threshold method. We proposed an adaptive threshold method which considering the characteristic of different sub-band, the method is adaptive to each sub-band. Experiment results show that the proposed method extracts white Gaussian noise from original signals in each step scale and eliminates the noise effectively. In addition, the method also preserves the detail information of the original image, obtaining superior quality image with higher peak signal to noise ratio(PSNR).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.269-286
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• 2018

Signal detection in symmetric alpha-stable ($S{\alpha}S$) distributed noise is a challenging problem. This paper proposes a detector based on a decreasing exponential function (DEF). The DEF detector can effectively suppress the impulsive noise and achieve good performance in the presence of $S{\alpha}S$ noise. The analytical expressions of the detection and false alarm probabilities of the DEF detector are derived, and the parameter optimization for the detector is discussed. A performance analysis shows that the DEF detector has much lower computational complexity than the Gaussian kernelized energy detector (GKED), and it performs better than the latter in $S{\alpha}S$ noise with small characteristic exponent values. In addition, the DEF detector outperforms the fractional lower order moment (FLOM)-based detector in $S{\alpha}S$ noise for most characteristic exponent values with the same order of magnitude of computational complexity.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.51-54
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• 1996

In this paper, an interchannel interference (ICI) and symbol error probability for orthogonal frequency division multiplexing (OFDM) on the two-ray fading environment are obtained analytically. From the analysis results, it is found that the ICI is a Gaussian random variable and its variance depends on the subchannel location, normalized time delay, and the number of subchannels. In addition, the OFDM signal without guard interveal is found to yield an irreducible error even at high signal to noise ratio due to the ICI.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.6
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• pp.1005-1012
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• 1987

An adaptive scalar DCT-Wiener filtering method that can be applied to the problem of restoring an image degraded by white Gaussian noise is presented. In this method, the power spectrum needed to Winer filering is adaptively estimated in block-wise according to the lical properties in transform domain. In addition, overlapping method for reducing the block artifact is considered. Experimental results show that the adaptive Wiener filter by the proposed method yields performance improvement and better image quality over the nonadaptive one and the spatial Lee filter.

• Journal of the Korean Society of Radiology
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• v.11 no.2
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• pp.69-77
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• 2017

This paper proposes a composite filter for noise reduction of image. To improve the image quality by reducing the noise in the liver ultrasound image, we tried to help the accurate image analysis. In the experiment, the top seven composite filters were selected by combining the Gaussian blur filter, the sharpening filter, and the median filter using the ATS-539 ultrasonic phantom, and applied to the ultrasound image in which this was done. As a result, it was found that the values of SNR, CNR and MSR all increased when the top seven composite filters were applied. In addition, PSNR of more than 30 dB, close to SSIM 1 showed that the image loss rate is small. Therefore, the appropriate application of the proposed composite filter in this research will be useful for accurate video reading and analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1162-1179
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• 2017

In this paper, we propose indoor location tracking method using RSSI(Received Signal Strength Indicator) value received from BLE(Bluetooth Low Energy) beacon. Due to the influence of various external environmental factors, it is very difficult to improve the accuracy in indoor location tracking. In order to solve this problem, we propose a novel method of reducing the noise generated in the external environment by using a double Gaussian filter. In addition, the value of the RSSI signal generated in the BLE beacon is different for each device. In this study, we propose a method to allocate additional weights in order to compensate the intensity of signal generated in each device. This makes it possible to improve the accuracy of indoor location tracking using beacons. The experiment results show that the proposed method effectively decrease the RSSI deviation and increase location accuracy. In order to verify the usefulness of this study, we compared the Kalman filter algorithm which is widely used in signal processing. We further performed additional experiments for application area for indoor location service and find that the proposed scheme is useful for BLE-based indoor location service.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.9
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• pp.1668-1679
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• 1994

The cochannel interference probability has been estimated both in the environment of Nakagami fading and Gasussian noise and in the environments of Gaussian noise and Nakagami fading plus log-normal shadowing. In noise and Nakagami fading environments, a theoretical analysis has been performed in order to calculate the cochannel interference probability in addition to computer simulation. In the environments of noise and Nakagami fading plus shadowing, only a simulation technique is used due to complexity in analysis. The spectrum efficiency is discussed on each case.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.568-574
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• 2002

The main issue of noise reduction of image is how to preserve edge and reduce noise. Usually, The Wiener falter is used for this purpose. But the conventional Wiener filter cannot remove noise well in both edge and smooth region due to the single size estimation window. In addition, it ignores the correlation between pixels. In this paper, we propose a new noise reduction algorithm, in which adaptive estimation window is used according to property of smooth region and edge region. In order to make edge more clear, directional Gaussian mask and directional estimation window combines to the Wiener filter according to direction of edge. From the simulation results, it can be seen that the proposed algorithm showed improves performance in both PSNR arid subjective evaluation

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.55-62
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• 2023

Radio waves must pass through the unstable atmosphere for successful wireless data transmission from space to ground stations. Data link algorithms required by the International Space Data Systems Advisory Committee (CCSDS) must be capable of detecting and resynchronizing cryptographic and receiver-side errors. However, error recovery is not part of the CCSDS requirements. This paper proposes an algorithm that enables robustness and error recovery against various noises. We experimented with environments such as Gaussian, Salt, Pepper, and S&P noise through noise reduction filters, filters that improve sharpness, and EDSR. In addition, we compare similar algorithms SES Alarmed and DSES Alarmed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.28-38
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• 2010

In order to remove the noise contained in recorded analog video, it is important to recognize the real characteristics and strength of the noise. This paper presents an efficient training-based noise reduction method for recorded analog video after analyzing the noise characteristics of analog video captured in a real broadcasting system. First we show that there is non-negligible noise correlation in recorded analog video and describe the limitations of the traditional noise estimation and reduction methods based on additive white Gaussian noise (AWGN) model. In addition, we show that auto-regressive (AR) model considering noise correlation can be successfully utilized to estimate and synthesize the noise contained in the recorded analog video, and the estimated AR parameters are utilized in the training-based noise reduction scheme to reduce the video noise. Experiment results show that the proposed method can be efficiently applied for noise reduction of recorded analog video with non-negligible noise correlation.