• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.745-748
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• 1998

Recently, image and voice processing part is using wavelet transform. We propose thining algorithm using wavelet tranform. Wavelet transform consists of low frequency and high frequency in the spatial and frequency domain. After the wavelet decomposition, more than 90 percents of energy are contained in lowest frequency band. Therefor, for images with large difference of gray value between foreground and background like character images, we can more accurately in the lowest frequency band. Lowest frequency band has wavelet transform significant coefficient(WTS) that is required for the thinning algorithm we proposed Paper [3][5][7][8] can not separate consonants and vowels of korean characters. Becuase korean characters have structural feature. This paper can separate consonants and vowels. Simulation executed low frequency image and data compression can reduce 1/4$^{n}$ with level n. we can redcue time complexity 3/8.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.260-263
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• 2003

The pseudocolor mage enhancement of gray image using wavelet filter coefficients is presented. The psuedocolor enhancement is that the decomposition enhancement is realized by wavelet transform and RGB image is extracted by wavelet filter coefficients with norma waveletl. The result of experiment an increases enhanced gray image as 30dB compared the processing of wavelet filter coefficients.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1129-1144
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• 2020

The thangka image inpainting method based on wavelet transform is not ideal for contour curves when the high frequency information is repaired. In order to solve the problem, a new image inpainting algorithm is proposed based on edge structural constraints and wavelet transform coefficients. Firstly, a damaged thangka image is decomposed into low frequency subgraphs and high frequency subgraphs with different resolutions using wavelet transform. Then, the improved fast marching method is used to repair the low frequency subgraphs which represent structural information of the image. At the same time, for the high frequency subgraphs which represent textural information of the image, the extracted and repaired edge contour information is used to constrain structure inpainting in the proposed algorithm. Finally, the texture part is repaired using texture synthesis based on the wavelet coefficient characteristic of each subgraph. In this paper, the improved method is compared with the existing three methods. It is found that the improved method is superior to them in inpainting accuracy, especially in the case of contour curve. The experimental results show that the hierarchical method combined with structural constraints has a good effect on the edge damage of thangka images.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.82-88
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• 2002

In this paper, we proposed a audio indexing method that is used wavelet transform for audio data retrieval. It is difficult for audio data to make a efficient audio data index because of its own particular properties, such as requirement of large storage, real time to transfer and wide bandwidth. An audio data in del using wavelet transform make it possible to index and retrieval by using the particular wavelet transform properties. Our proposed indexing method doesn't separate data to several blocks. Therefore we use both high-pass and low-pass parts of last level coefficient of wavelet transform. Audio data indexing is made by applying the string matching algorithm to high-pass part and zero-crossing histogram to low-pass part. These are transformed to the continued strings, Through this method, we described a retrieval efficiency. The retrieval method is done by comparing the database index string to the query string and then data of minimum values is chosen to the result. Our simulation decided proper comparative coefficient and made known changing of retrieval efficiency versus audio data length. The results show that the proposed method improves retrieval efficiency compared to conventional method.

• Journal of the Korean Society of Radiology
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• v.2 no.1
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• pp.23-29
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• 2008

The present study proposed a method that dissolves ultrasonographic images into multiple resolutions using wavelet conversion and a boundary detection filter and improves the quality of ultrasonographic images through boundary detection filtering. In order to reduce noises and strengthen edges, the proposed method adjusted selectivity coefficient by area step by step from a low resolution image obtained from wavelet converted images to a high resolution image and performed edge filtering in consideration of direction. Through this method, we generated a selective low pass filtering effect in areas except edges by decreasing the wavelet coefficient for pixels in spot areas, improved continuity by smoothing edges in the tangential direction, and enhanced contrast by thinning in the normal direction. Through an experiment, we compared the filtering method using a non linear anisotropic expansion model and the filtering method using wavelet contraction structure in single resolution.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.6
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• pp.96-103
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• 1999

This paper proposes efficient VLSI architecture for computation of the 1-D discrete wavelet transform (DWT). The proposed VLSI architecture computes the wavelet lowpass and highpass output sequences using the product term anhm, $n,m{\ge}0$, where an and hm denote the imput sequence and the wavelet lowpass filter coefficient, respectively. Whereas the conventional architectures compute the lowpass and highpass output sequences using the product terms anhm and angm, respectively, where gm denotes the wavelet highpass filter coefficient. The proposed architecture is applied to computation of the Daubechies 4-tap wavelet transform using the relationships between the Daubechies wavelet filter coefficients. Performance comparison of various architectures for computation of the 1-D DWT are presented. Note that the proposed architecture does not require extra processing units whereas the conventional architectures need them. Also it is modeled in very high speed integrated circuit hardware description language (VHDL) and simulated to show its functional validity.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.31-37
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• 1998

This paper proposes a feature extraction method using wavelet transform for speech recognition. Speech recognition system generally carries out the recognition task based on speech features which are usually obtained via time-frequency representations such as Short-Time Fourier Transform (STFT) and Linear Predictive Coding(LPC). In some respects these methods may not be suitable for representing highly complex speech characteristics. They map the speech features with same may not frequency resolutions at all frequencies. Wavelet transform overcomes some of these limitations. Wavelet transform captures signal with fine time resolutions at high frequencies and fine frequency resolutions at low frequencies, which may present a significant advantage when analyzing highly localized speech events. Based on this motivation, this paper investigates the effectiveness of wavelet transform for feature extraction of wavelet transform for feature extraction focused on enhancing speech recognition. The proposed method is implemented using Sampled Continuous Wavelet Transform (SCWT) and its performance is tested on a speaker-independent isolated word recognizer that discerns 50 Korean words. In particular, the effect of mother wavelet employed and number of voices per octave on the performance of proposed method is investigated. Also the influence on the size of mother wavelet on the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is compared with the most prevalent conventional method, MFCC (Mel0frequency Cepstral Coefficient). The experiments show that the recognition performance of the proposed method is better than that of MFCC. But the improvement is marginal while, due to the dimensionality increase, the computational loads of proposed method is substantially greater than that of MFCC.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.728-731
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• 1997

This paper discusses approximation modelling of discrete-time linear time-varying system(LTVS). The wavelet transform is considered as a tool for representing and approximating a LTVS. The joint time-frequency properties of wave analysis are appropriate for describing the LTVS. Simulation results is included to illustrate the potential application of the technique.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.851-854
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• 1998

Recently efficient image coding algorithms using zerotree have been proposed. In these methods, the locations of nonzero wavelet coefficients are encoded with a tree structure, called zerotree, which can exploit the self-similarity of the wavelet pyramid decomposition across different scales. These are very effective, especially in low bit rate image coding. In this paper, two zerotree image coding algorithms, EZW and SPIHT, are briefly introduced, and a new zerotree searching scheme is proposed to emphasize the significance of a wavelet coefficient by its orientations as well as its scale.

• East Asian mathematical journal
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• v.26 no.1
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• pp.105-113
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• 2010

In the spectral analysis, Fourier coeffcients are very important to give informations for the original signal f on a finite domain, because they recover f. Also Fourier analysis has extension to wavelet analysis for the whole space R. Various kinds of reconstruction theorems are main subject to analyze signal function f in the field of wavelet analysis. In this paper, we will present a new reconstruction theorem of functions in $L^1(R)$ using a nonharmonic Fourier series. When we construct this series, we have used dyadic subdivision schemes.