• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.519-522
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• 2000

Digital watermarking has been proposed as a solution to the problem of copyright protection of the multimedia documents. In this paper a new watermarking method for digital images operating in the frequency domain is proposed. In our approach, DCT coefficients of the watermark are added to the low frequency region of the host image, and extract it using the Wiener Filter. Due to the characteristic of the wiener filtering, the watermark is robust to various image processing techniques. Experimental results show that it is possible to reliably extract the watermark without degrading image quality.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1290-1294
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• 2008

We present a novel deblurring algorithm for bi-level images blurred by some parameterizable point spread function. The proposed method iteratively searches unknown parameters in the point spread function and noise-to-signal ratio by minimizing an objective function that is based on the binariness and the difference between two intensity values of restoring image. In simulations and experiments, the proposed method showed improved performance compared with the Wiener filtering based method in terms of bit error rate after segmentation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.686-687
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• 2017

본 논문에서는 잡음에 강인하고 음성인식 성능이 효과적인 멜 주파수 켑스트럼 계수의 파라미터의 추출 알고리즘을 제안한다. 본 논문에서 제안한 알고리즘은 배경잡음이 혼합된 깨끗한 연속음성 중에서 위너필터를 이용하여 음성에 포함된 배경잡음을 감소시키며, 이후에 멜 주파수 켑스트럼 계수의 특징추출 방법을 사용하여 음성의 특징 파라미터를 추출한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.127-129
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• 2012

Due to the prevalence of digital camera, multi-media etc. the image is being used in everyday life. However, noise always damages the image and the image denoising technology is important part for improving the image visual quality. There are many existing methods to remove noise such as wiener filter, mean filter and VisuShrink etc. However, they perform not good enough for denoising. Hence, in this paper we proposed a hybrid filter algorithm which consists of wiener filter and modified wavelet based thresholding method using adaptive threshold and thresholding function. The proposed algorithm shows not only better low frequency and high frequency property, but also the outstanding noise suppression and edge preservation properties.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1400-1412
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• 2000

In this paper, we propose a technique for image enhancement, especially for denoising and deblocking based on wavelets. In this proposed algorithm, frame wavelet system designed as a optimal edge detector was used. And our theory depends on Lipschitz regularity, spatial correlation, and some important assumptions. The performance of the proposed algorithm was compared with three popular test images in image processing area. Experimental results show that the performance of the proposed algorithm was better than other previous denoising techniques like spatial averaging filter, Gaussian filter, median filter, Wiener filter, and some other wavelet based filters in the aspect of both PSNR and human visual system, The experimental results also show approximately the same capability of deblocking as the previous developed techniques

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1227-1236
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• 2012

The low speed machinery faults are usually caused by the bearing failure of the rolling elements. As the life time of the bearing is limited, the condition monitoring of bearing is very important to maintain the continuous operation without failures. A few monitoring techniques using time domain, frequency domain and fuzzy neural network vibration analysis are introduced to detect and diagnose the faults of the low speed machinery. This paper presents a method of fault detection for the rolling element bearing in the low speed machinery using the Wiener filtering and shock pulse counting techniques. Wiener filter is used for noise cancellation and it clearly makes the shock pulse emerge from the time signal with the high level of noise. The shock pulse counting is used to determine the various faults obviously from the shock signal with transient pulses not related with the bearing fault. Machine fault simulator is used for the experimental measurement in order to verify this technique is the powerful tool for the low speed machine compared with the frequency analysis. The test results show that the method proposed is very effective parameter even for the signal with high contaminated noise, speed variation and very low energy. The presented method shows the optimal tool for the condition monitoring purpose to detect the various bearing fault with high accuracy.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.73-83
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• 2014

Thanks to the development of diverse audio editing Technology, audio file can be easily revised. As a result, diverse social problems like forgery may be caused. Digital forensic technology is actively studied to solve these problems. In this paper, a hand-held device identification method, an area of digital forensic technology is proposed. It uses the noise features of devices caused by the design and the integrated circuit of each device but cannot be identified by the audience. Wiener filter is used to get the noise sounds of devices and their acoustic features are extracted via MIRtoolbox and then they are trained by multi-layer neural network. To evaluate the proposed method, we use 5-fold cross-validation for the recorded data collected from 6 mobile devices. The experiments show the performance 99.9%. We also perform some experiments to observe the noise features of mobile devices are still useful after the data are uploaded to UCC. The experiments show the performance of 99.8% for UCC data.

• Journal of radiological science and technology
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• v.44 no.3
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• pp.225-229
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• 2021

The digital image such as medical X-ray and nuclear medicine field mainly contains noise distribution. The noise degree in image degrades image quality. That is why, the noise reduction algorithm is efficient for medical image field. In this study, we confirmed effectiveness of application for median modified Wiener filter (MMWF) algorithm for noise reduction in PET/MR image compared with median filter image, which is used as conventional noise redcution algorithm. The Jaszczak PET phantom was used by using 18F solution and filled with NaCl+NiSO4 fluids. In addition, the radioactivity ratio between background and six spheres in the phantom is maintained to 1:8. In order to mimic noise distribution in the image, we applied Gaussian noise using MATLAB software. To evlauate image quality, the contrast to noise ratio (CNR) and coefficient of variation (COV) were used. According to the results, compared with noise image and images with MMWF algorithm, the image with MMWF algorithm is increased approximately 33.2% for CNR result, decreased approximately 79.3% for COV result. In conclusion, we proved usefulness of MMWF algorithm in the PET/MR images.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.213-218
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• 2021

It is known that total least-squares method shows better estimation performance than least-squares method when noise is present at the input and output at the same time. When total least squares method is applied to data with time series characteristics, Recursive Total Least Squares (RTS) algorithm has been proposed to improve the real-time performance. However, RTLS has numerical instability in calculating the inverse matrix. In this paper, we propose an algorithm for reducing numerical instability as well as having similar convergence to RTLS. For this algorithm, we propose a new RTLS using Iterative Wiener Filter (IWF). Through the simulation, it is shown that the convergence of the proposed algorithm is similar to that of the RTLS, and the numerical robustness is superior to the RTLS.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1641-1648
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• 2017

The main purpose of this paper is to compare the performances of various filters with color images to remove the noise. Furthermore, we suggest a modified de-noising process by the transformation of color model from RGB to another color models, such as HSV and $YC_BC_R$, to improve the quality of de-noising methods encompassing Median, Wiener, and Mean filters. Neither the performance comparison of the de-noising filters with color images nor the converting the color model for better de-noise on the degraded images haven't been performed before. Inspired to make improvements, we conduct experiments with new de-noising process on color images. The result of the experiments is shown that it could assist on certain filters being more reliable techniques.