• Journal of the Korean Society of Radiology
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• v.15 no.3
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• pp.345-353
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• 2021

Digital radiography imaging systems can also help diagnose lesions in patients, but if x-rays that enter the human body cause scatter x-ray due to interaction with substances, they affect the signal and noise characteristics of digital x-ray images. To regard the human body as polymethyl methacrylate (PMMA) and observe the properties of scattered x-ray generated from PMMA on x-ray images, we analyze signal and noise in the spatial domain as well as noise-power spectrum (NPS), and detective quantum efficiency (DQE) at zero frequency. As PMMA thickness increased, signals decreased, the noise increased, and NPS degradation was identified in overall spatial frequencies. Based on these characteristics, zero-frequency performance was also shown to be degraded. Comparative analysis with Monte-carlo simulations will need to be made to analyze the zero-frequency performance by scattered x-ray of indirect conversion-type x-ray detectors more quantitatively.

• Journal of radiological science and technology
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• v.32 no.1
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• pp.17-24
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• 2009

Noise evaluation for an image has been performed by root mean square (RMS) granularity, autocorrelation function (ACF), and Wiener spectrum. RMS granularity stands for standard deviation of photon data and ACF is acquired by integration of 1 D function of distance variation. Fourier transform of ACF results in noise power spectrum which is called Wiener spectrum in image quality evaluation. Wiener spectrum represents noise itself. In addition, along with MTF, it is an important factor to produce detective quantum efficiency (DQE). The proposed evaluation method using Wiener spectrum is expected to contribute to educate the concept of Wiener spectrum in educational organizations, choose the appropriate imaging detectors for clinical applications, and maintain image quality in digital imaging systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.113-122
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• 2016

In this paper, we suggest a approach which comprises fast Fourier transform inversion by wavelet noise attenuation. It represents an inverse filtering by adopting a factor into the Wiener filtering, and the optimal factor is chosen to minimize the overall mean squared error. in order to apply the Wiener filter, we have to compute the power spectrum of original image from the corrupted figure. Since the Wiener filtering contains the inverse filtering process, it expands the noise when the blurring filter is not invertible. To remove the large noises, the best is to remove the noise using wavelet threshold. Wavelet noise attenuation steps are consisted of inverse filtering and noise reduction by Wavelet functions. experimental results have not outperformed the other methods over the overall restoration performance.

• Journal of the Korean Society of Radiology
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• v.7 no.3
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• pp.227-232
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• 2013

The pixels used in a digital X-ray detector have different sensitivities and offset values. A non-uniform image is consequently obtained. Flat-field correction was introduced to resolve this problem and carried out image preprocessing in a digital imaging system. Nevertheless, the non-uniform images caused by several reasons have been being occasionally acquired. In this study, the non-uniform images acquired in digital imaging systems were applied to flat-field correction, and NPSs were calculated and analyzed with those images before and after correction. It was confirmed that low frequency noise were effectively eliminated.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.1
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• pp.45-52
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• 2016

This paper suggests a new method of finding regularization parameter for image restoration problems. Wiener filter requires priori information such that power spectrums of original image and noise. Constrained least squares restoration also requires knowledge of the noise level. If the prior information is not available, separate optimization functions for Tikhonov regularization parameter are suggested in the literature such as generalized cross validation and L-curve criterion. In this paper, self-regularization method that connects bias term of augmented linear system and smoothing term of Tikhonov regularization is introduced in the frequency domain and applied to the image restoration problems. Experimental results show the effectiveness of the proposed method.

• Journal of IKEEE
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• v.14 no.2
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• pp.16-23
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• 2010

Since the interest of a brain science research is increased recently, various devices using brain waves have been developed in the field of brain training game, education application and brain computer interface. In this paper, we have developed a portable EEG measurement and a bluetooth based wireless transmission device measuring brain waves from the frontal lob simply and conveniently. The low brain signals about 10~100${\mu}V$ was amplified into several volts and low pass, high pass and notch filter were designed for eliminating unwanted noise and 60Hz power noise. Also, PIC24F192 microcontroller has been used to convert analog brain signal into digital signal and transmit the signal into personal computer wirelessly. The sampling rate of 1KHz and bluetooth based wireless transmission with 38,400bps were used. The LabVIEW programing was used to receive and monitor the brain signals. The power spectrum of commercial biopac MP100 and that of a developed EEG system was compared for performance verification after the simulation signals of sine waves of $1{\mu}V$, 0~200Hz was inputed and processed by FFT transformation. As a result of comparison, the developed system showed good performance because frequency response of a developed system was similar to that of a commercial biopac MP100 inside the range of 30Hz specially.