• Title/Summary/Keyword: Wavelet domain

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A new approach for contrast enhancement using the properties of wavelet coefficients (웨이블릿 계수 특성을 이용한 대비 개선에 관한 연구)

  • Park, Tae-Jun;Eom, Min-Young;Choe, Yun-Sik
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.175-177
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    • 2004
  • The current JPEG-2000 standard is a wavelet based scheme because wavelet transform have some advantages compare to DCT transform. In compressed images, there are some image degradation factors like contrast distortion by Quantization process. This factor is very important to HVS (Human Visual System). Therefore, In this paper, we propose a new algorithm for contrast enhancement using the properties of wavelet coefficients. This algorithm is processed in the wavelet domain and so it can be applied efficiently to JPEG-2000.

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A Study on the Time-Frequency Analysis of Transient Signal using Wavelet Transformation (Wavelet 변환을 이용한 과도신호의 시간-주파수 해석에 관한 연구)

  • 이기영;박두환;정종원;김기현;이준탁
    • Proceedings of the Korean Society of Marine Engineers Conference
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    • 2002.05a
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    • pp.219-223
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    • 2002
  • Voltage and current signals during impulse tests on transformer are treated as non-stationary signals. A new method incorporating signal-processing method such as Wavelets and courier transform is proposed for failure identification. It is now possible to distinguish failure during impulse tests. The method is experimentally validated on a transformer winding. The wavelet transforms enables the detection of the time of occurrence of switching or failure events. After establishing the time of occurrence, the original waveform is split into two or more sections. The wavelet transform has ability to analysis the failure signal on time domain as well as frequency domain. Therefore, the wavelet transform is superior than courier transform to analysis the failure signal. In this paper, the fact was proved by real data which was achieved.

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A Wavelet based Feature Selection Method to Improve Classification of Large Signal-type Data (웨이블릿에 기반한 시그널 형태를 지닌 대형 자료의 feature 추출 방법)

  • Jang, Woosung;Chang, Woojin
    • Journal of Korean Institute of Industrial Engineers
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    • v.32 no.2
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    • pp.133-140
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    • 2006
  • Large signal type data sets are difficult to classify, especially if the data sets are non-stationary. In this paper, large signal type and non-stationary data sets are wavelet transformed so that distinct features of the data are extracted in wavelet domain rather than time domain. For the classification of the data, a few wavelet coefficients representing class properties are employed for statistical classification methods : Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Network etc. The application of our wavelet-based feature selection method to a mass spectrometry data set for ovarian cancer diagnosis resulted in 100% classification accuracy.

Speckle Noise Reduction for Ultrasonic Images Using Homomorphic Wavelet-based MMSE Filter (호모모르픽 웨이브렛 기반 MMSE 필터를 이용한 초음파영상의 스펙클 잡음 제거)

  • 박원용;장익훈;김남철
    • Proceedings of the IEEK Conference
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    • 2000.09a
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    • pp.679-682
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    • 2000
  • In this paper, a MMSE filter in homomorphic wavelet transform domain is proposed for restoring an ultrasonic images corrupted by speckle noise. In order to remove effectively the speckle noise which is a kind of multiplicative noise, speckle noise is transformed into a form of additive noise and then the additive noise is denoised through the MMSE filter in homomorphic wavelet transform domain. The proposed method shows much higher quality in terms of ISNR and subject quality.

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PROPERTIES OF RANDOM SIGNALS IN WAVELET DOMAIN

  • Lee, Young Seock;Kim, Sung Hwan
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.3 no.1
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    • pp.107-114
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    • 1999
  • In many applications (e,g., identification of non-destructive testing signal and biomedical signal and multiscale analysis of image), it is of interest to analyze and identify phenomena occurring at the different scales. The recently introduced wave let transforms provide a time-scale decomposition of signals that offers the possibility of such signals. However, there is no corresponding statistical properties to development of multiscale statistical signal processing. In this paper, we derive such properties of random signals in wavelet domain.

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Demosaicking Using Weighted Sum in Wavelet domain (가중치 합을 이용한 웨이블릿 영역의 디모자이킹)

  • Jeong, Bo-Gyu;Eom, Il-Kyu
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.821-822
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    • 2008
  • This paper presents a new demosaicking method based on weighted sum in the wavelet domain. In our method, the missing wavelet coefficients in lowest frequency subband are obtained by weighted sum. Since detail coefficients have large values at the edge region, these values are used as weighting factors. Detail coefficients are replaced by the coefficients in the corresponding subbands. Experimental results show that the proposed method generates good performance.

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Multi-scale Correlation Analysis between Sea Level Anomaly and Climate Index through Wavelet Approach (웨이블릿 접근을 통한 해수면 높이와 기후 지수간의 다중 스케일 상관 관계 분석)

  • Hwang, Do-Hyun;Jung, Hahn Chul
    • Korean Journal of Remote Sensing
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    • v.38 no.5_1
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    • pp.587-596
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    • 2022
  • Sea levels are rising as a result of climate change, and low-lying areas along the coast are at risk of flooding. Therefore, we tried to investigate the relationship between sea level change and climate indices using satellite altimeter data (Topex/Poseidon, Jason-1/2/3) and southern oscillation index (SOI) and the Pacific decadal oscillation (PDO) data. If time domain data were converted to frequency domain, the original data can be analyzed in terms of the periodic components. Fourier transform and Wavelet transform are representative periodic analysis methods. Fourier transform can provide only the periodic signals, whereas wavelet transform can obtain both the periodic signals and their corresponding time location. The cross-wavelet transformation and the wavelet coherence are ideal for analyzing the common periods, correlation and phase difference for two time domain datasets. Our cross-wavelet transform analysis shows that two climate indices (SOI, PDO) and sea level height was a significant in 1-year period. PDO and sea level height were anti-phase. Also, our wavelet coherence analysis reveals when sea level height and climate indices were correlated in short (less than one year) and long periods, which did not appear in the cross wavelet transform. The two wavelet analyses provide the frequency domains of two different time domain datasets but also characterize the periodic components and relative phase difference. Therefore, our research results demonstrates that the wavelet analyses are useful to analyze the periodic component of climatic data and monitor the various oceanic phenomena that are difficult to find in time series analysis.

Wavelet Power Spectrum Estimation for High-resolution Terahertz Time-domain Spectroscopy

  • Kim, Young-Chan;Jin, Kyung-Hwan;Ye, Jong-Chul;Ahn, Jae-Wook;Yee, Dae-Su
    • Journal of the Optical Society of Korea
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    • v.15 no.1
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    • pp.103-108
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    • 2011
  • Recently reported asynchronous-optical-sampling terahertz (THz) time-domain spectroscopy enables high-resolution spectroscopy due to a long time-delay window. However, a long-lasting tail signal following the main pulse is often measured in a time-domain waveform, resulting in spectral fluctuation above a background noise level on a high-resolution THz amplitude spectrum. Here, we adopt the wavelet power spectrum estimation technique (WPSET) to effectively remove the spectral fluctuation without sacrificing spectral features. Effectiveness of the WPSET is verified by investigating a transmission spectrum of water vapor.

Wavelet-Based Fast Fractal Image Compression with Multiscale Factors (레벨과 대역별 스케일 인자를 갖는 웨이브릿 기반 프랙탈 영상압축)

  • 설문규
    • Journal of the Korea Computer Industry Society
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    • v.4 no.4
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    • pp.589-598
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    • 2003
  • In the conventional fractal image compression in the DWT(discrete wavelet transform), the domain and range blocks were classified as B${\times}$B block size first before all domain block for each range block was searched. The conventional method has a disadvantages that the encoding time takes too long, since the domain block for entire image was searched. As an enhancement to such inefficiencies and image quality, this paper proposes wavelet-based fractal image compression with multiscale factors. Thus, this proposed method uses multiscale factor along each level and band to enhance an overall image quality. In encoding process of this method, the range blocks are not searched for all the domain blocks; however, using the self affine system the range blocks are selected from the blocks in the upper level. The image qualify of the conventional method is 32.30[dB], and the proposed method is 35.97[dB]. The image quality is increased by 3.67[dB].

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A Study on Blind Watermarking Technique of Digital Image using 2-Dimensional Empirical Mode Decomposition in Wavelet Domain (웨이블릿 평면에서의 2D-EMD를 이용한 디지털 영상의 블라인드 워터마킹 기술에 관한 연구)

  • Lee, Young-Seock;Kim, Jong-Weon
    • Journal of Internet Computing and Services
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    • v.11 no.2
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    • pp.99-107
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    • 2010
  • In this paper a blind watermarking algorithm for digital image is presented. The proposed method operates in wavelet domain. The watermark is decomposed into 2D-IMFs using BEMD which is the 2-dimensional extension of 1 dimensional empirical mode decomposition. The CDMA based on SS technique is applied to watermark embedding and detection process. In the watermark embedding process, each IMF of watermark is embedded into middle frequency subimages in wavelet domain, so subimages just include partial information about embedded watermark. By characteristics of BEMD, when the partial information of watermark is synthesized, the original watermark is reconstructed. The experimental results show that the proposed watermarking algorithm is imperceptible and moreover is robust against JPEG compression, common image processing distortions.