• Convergence Security Journal
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• v.7 no.1
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• pp.83-89
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• 2007

This paper is to suggest the method of embedding a signature to pictures secretly under the orthogonal wavelet transform which represents pictures as multi-resolution representations. As it is focused upon the differential output under the multi-resolution representation of pictures, this method can embed bit series to pictures. In doing so, it can compound approximately 6K byte of information with gray-level image $256{\times}256$. The method can include not only the database which designates copyright of pictures but also the author and usage of pictures, and the information of the picture itself. Therefore, this method can easily discriminate the inspection of picture database.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.10 no.4
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• pp.13-19
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• 2000

With the rapid development of the information communication technology, more and more distribution multimedia data and electronic publishing in the web, has created a need for the copyright protection with authentication of digital information. In this paper, we propose a multi-watermarking adding and adaptive spectral watermark algorithm well adaptive frequency domain of each hierarchical using orthogonal forward wavelet transform(FWT. Numerical test results, created watermarking image robustness not only image transform such as low-pass filtering, bluring, sharpen filtering, wavelet compression but also brightness, contrast gamma correction, histogram equalization, cropping.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.16-23
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• 2002

In this paper, we propose the growing and pruning algorithm to design the optimal structure of modular wavelet neural network(MWNN) with F-projection and geometric growing criterion. Geometric growing criterion consists of estimated error criterion considering local error and angle criterion which attempts to assign wavelet function that is nearly orthogonal to all other existing wavelet functions. These criteria provide a methodology which a network designer can construct MWNN according to one's intention. The proposed growing algorithm increases in number of module or the size of modules of MWNN. Also, the pruning algorithm eliminates unnecessary node of module or module from constructed MWNN to overcome the problem due to localized characteristic of wavelet neural network which is used to modules of MWNN. We apply the proposed constructing algorithm of the optimal structure of MWNN to approximation problems of 1-D function and 2-D function, and evaluate the effectiveness of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.52-61
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• 2006

Orthogonal wavelet tansform which is generally used in image and signal processing applications has limited performance because of lack of shift invariance and low directional selectivity. To overcome these demerits complex wavelet transform has been proposed. In this paper, we present an efficient image denoising method using dual-tree complex wavelet transform and Bernoulli-Gauss prior model. In estimating hyper-parameters for Bernoulli-Gaussian model, we present two simple and non-iterative methods. We use hypothesis-testing technique in order to estimate the mixing parameter, Bernoulli random variable. Based on the estimated mixing parameter, variance for clean signal is obtained by using maximum generalized marginal likelihood (MGML) estimator. We simulate our denoising method using dual-tree complex wavelet and compare our algorithm to well blown denoising schemes. Experimental results show that the proposed method can generate good denoising results for high frequency image with low computational cost.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.89-95
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• 2017

UFMC is known as a candidate for the 5G wireless communication system because it is robust against ICI and better performs in asynchronous situation than OFDM. In the UFMC system, the filtering is performed for each subband so the performance of the filter is very important. The Dolph-Chebyshev filter has been used in conventional UFMC system because of its small out-of-band radiation. However it has distortion in the sub-band and skirt characteristics is not good enough. Therefore, it is necessary to study a new type of UFMC filter which reduces the distortion in the subband and has sharp skirt characteristics. In this paper we analyze the effect of filter frequency response in UFMC system and suggest the wavelet based type of filter that substitutes the Dolph-ChebyShev filter used in the conventional UFMC system. The simulation results show that wavelet filter has better BER performance in multipath fading channels than conventional filters.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.1207-1214
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• 2008

We give an elementary proof of one of the main results in [H.O. Kim, R.Y. Kim, J.K. Lim, The infimum cosine angle between two finitely generated shift-invariant spaces and its applications, Appl. Comput. Har-mon. Anal. 19 (2005) 253-281] concerning the existence of an oblique projection onto a finitely generated shift-invariant space along the orthogonal complement of another finitely generated shift-invariant space under the assumption that the generators generate Riesz bases.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.267-275
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• 2004

While the convergence of the classical Fourier series has been well known, the rate of its convergence is not well acknowledged. The results regarding the rate of convergence of the Fourier series and wavelet expansions can be found in the book of Walter[5]. In this paper, we give the rate of convergence of hybrid sampling series associated with orthogonal wavelets.

• Journal of Broadcast Engineering
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• v.4 no.2
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• pp.144-154
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• 1999

The binomial sequences are family of orthogonal sequences that can be generated with remarkable simplicity-no multiplications are necessary. This paper introduces a class of non-recursive multidimensional filters for frequency-selective image processing without multiplication operations. The magnitude responses are narrow-band. approximately gaussian-shaped with center frequencies which can be positioned to yield low-pass. band-pass. or high-pass filtering. Algorithms for the efficient implementation of these filters in software or in hardware are described. Also. we show that the binomial QMFs are the maximally flat magnitude square Perfect Reconstruction paraunitary filters with good compression capability and these are shown to be wavelet filters as well. In wavelet transform the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal direction and maintains constant the number of pixels required to describe the images. An efficient perfect reconstruction binomial QMF-Wavelet signal decomposition structure is proposed. The technique provides a set of filter solutions with very good amplitude responses and band split. The proposed binomial QMF-filter structure is efficient, simple to implement on VLSl. and suitable for multi-resolution signal decomposition and coding applications.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.47-54
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• 2005

In general, wavelet coefficients are composed of a few large coefficients and a lot of small coefficients. In this paper, we propose image denoising algorithm using Bernoulli-Gaussian mixture model based on sparse characteristic of wavelet coefficient. The Bernoulli-Gaussian mixture is composed of the multiplication of Bernoulli random variable and Gaussian mixture random variable. The image denoising is performed by using Bayesian estimation. We present an effective denoising method through simplified parameter estimation for Bernoulli random variable using local expected squared error. Simulation results show our method outperforms the states-of-art denoising methods when using orthogonal wavelets.