• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.34-42
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• 2006

In general, almost information is stored in only a few wavelet coefficients. This sparse characteristic of wavelet coefficient can be modeled by the mixture of Gaussian probability density function and point mass at zero, and denoising for this prior model is peformed by using Bayesian estimation. In this paper, we propose a method of parameter estimation for denoising using hypothesis-testing problem. Hypothesis-testing problem is applied to variance of wavelet coefficient, and $X^2$-test is used. Simulation results show our method outperforms about 0.3dB higher PSNR(peak signal-to-noise ratio) gains compared to the states-of-art denoising methods when using orthogonal wavelets.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.14-16
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• 2007

The statistics for the Gaussian noise mixed with impulsive noise are modelled. The denoising algorithm called amplitude-limited sample average filter is derived, which is optimal in terms of minimizing mean square errors under the assumption that contaminating noise is heavy-tailed Gaussian distributed. Its performance is shown to be excellent when image is corrupted mainly with Gaussian noise. However, it shows visually grainy output as the amount of impulsive noise increases. In order to overcome such problems, it is combined with the myriad filter to propose an amplitude-limited myriad filter. Simulation shows it effectively removes both Gaussian and impulsive noise, not blurring edges severey.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.246-249
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• 2009

Due to the additive white Gaussian noise (AWGN), images are often corrupted. In recent days, Bayesian estimation techniques to recover noisy images in the wavelet domain have been studied. The probability density function (PDF) of an image in wavelet domain can be described using highly-sharp head and long-tailed shapes. If a priori probability density function having the above properties would be applied well adaptively, better results could be obtained. There were some frequently proposed PDFs such as Gaussian, Laplace distributions, and so on. These functions model the wavelet coefficients satisfactorily and have its own of characteristics. In this paper, mixture distributions of Gaussian and Laplace distribution are proposed, which attempt to corporate these distributions' merits. Such mixture model will be used to remove the noise in images by adopting Maximum a Posteriori (MAP) estimation method. With respect to visual quality, numerical performance and computational complexity, the proposed technique gained better results.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1326-1335
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• 2021

As part of the cell division method, we proposed a method for segmenting images generated by topography microscopes through deep learning-based feature generation and graph segmentation. Hybrid vector shapes preserve the overall shape and boundary information of cells, so most cell shapes can be captured without any post-processing burden. NIH-3T3 and Hela-S3 cells have satisfactory results in cell description preservation. Compared to other deep learning methods, the proposed cell image segmentation method does not require postprocessing. It is also effective in preserving the overall morphology of cells and has shown better results in terms of cell boundary preservation.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.60-65
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• 2016

This paper introduces the non-local means (NLM) algorithm for image denoising, and also introduces an improved algorithm which is based on the principal component analysis (PCA). To do the PCA, a covariance matrix of a given image should be evaluated first. If we let the size of neighborhood patches of the NLM S × S², and let the number of pixels Q, a matrix multiplication of the size S² × Q is required to compute a covariance matrix. According to the characteristic of images, such computation is inefficient. Therefore, this paper proposes an efficient method to compute the covariance matrix by sampling the pixels. After sampling, the covariance matrix can be computed with matrices of the size S² × floor (Width/l) × (Height/l).

• 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

• Journal of architectural history
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• v.31 no.1
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• pp.41-50
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• 2022

Traditional wooden buildings deform over time and are vulnerable to fire or earthquakes. Therefore, traditional wooden buildings require continuous management and repair, and securing architectural drawings is essential for repair and restoration. Unlike modernized CAD drawings, traditional wooden building drawings scan and store hand-drawn drawings, and in this process, many noise is included due to damage to the drawing itself. These drawings are digitized, but their utilization is poor due to noise. Difficulties in systematic management of traditional wooden buildings are increasing. Noise removal by existing algorithms has limited drawings that can be applied according to noise characteristics and the performance is not uniform. This study presents deep artificial neural network based noised reduction for architectural drawings. Front/side elevation drawings, floor plans, detail drawings of Korean wooden treasure buildings were considered. First, the noise properties of the architectural drawings were learned with both a cycle generative model and heuristic image fusion methods. Consequently, a noise reduction network was trained through supervised learning using training sets prepared using the noise models. The proposed method provided effective removal of noise without deteriorating fine lines in the architectural drawings and it showed good performance for various noise types.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.145-155
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• 2015

Noise reduction has been actively studied in the digital image processing and recently, block-based denoising algorithms are widely used. In particular, a low rank approximation employing WNNM(Weighted Nuclear Norm Minimization) and block-based approaches demonstrated the potential for effective noise reduction. However, the algorithm based on low rank a approximation generates the artifacts in the image restoration step. In this paper, we analyzes the image content using the STFT(Short Time Fourier Transform) and proposes an effective method of minimizing the artifacts generated from the conventional algorithm. To evaluate the performance of the proposed scheme, we use the test images containing a wide range of noise levels and compare the results with the state-of-art algorithms.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.308-318
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• 2022

Noise is a serious issue. While sending images via electronic communication, Impulse noise, which is created by unsteady voltage, is one of the most common noises in digital communication. During the acquisition process, pictures were collected. It is possible to obtain accurate diagnosis images by removing these noises without affecting the edges and tiny features. The New Average High Noise Density Median Filter. (HNDMF) was proposed in this paper, and it operates in two steps for each pixel. Filter can decide whether the test pixels is degraded by SPN. In the first stage, a detector identifies corrupted pixels, in the second stage, an algorithm replaced by noise free processed pixel, the New average suggested Filter produced for this window. The paper examines the performance of Gaussian Filter (GF), Adaptive Median Filter (AMF), and PHDNF. In this paper the comparison of known image denoising is discussed and a new decision based weighted median filter used to remove impulse noise. Using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structure Similarity Index Method (SSIM) metrics, the paper examines the performance of Gaussian Filter (GF), Adaptive Median Filter (AMF), and PHDNF. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the HNDMF model has reached to a better performance with the maximum picture quality. images affected by various amounts of pretend salt and paper noise, as well as speckle noise, are calculated and provided as experimental results. According to quality metrics, the HNDMF Method produces a superior result than the existing filter method. Accurately detect and replace salt and pepper noise pixel values with mean and median value in images. The proposed method is to improve the median filter with a significant change.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.109-115
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• 2001

Wavelet coefficients are known to have decorrelating properties, since wavelet is orthonormal transformation. but empirically, those wavelet coefficients of images, like edges, are not statistically independent. Jansen and Bultheel(1999) developed the empirical Bayes approach to improve the classical threshold algorithm using local characterization in Markov random field. They consider the clustering of significant wavelet coefficients with uniform distribution. In this paper, we developed wavelet thresholding algorithm using Laplacian distribution which is more realistic model.