• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.310-319
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• 2010

In this paper, digital watermarking method using wavelet transform and quad-tree algorithm is proposed. The proposed algorithm transforms the input image by DWT(Discrete Wavelet Transform) and AWPT(Adaptive Wavelet Packet Transform), inserts the watermark by quad-tree algorithm and the Cox's algorithm. The simulation for performance analysis of the proposed algorithm is implemented about the effect of embedding watermark in each subband coefficient (HH, LH, HL) of DWT, each DWT level, and each AWPT level. The simulation result by using DWT is compared with that using AWPT in the proposed algorithm. In addition, the effect of embedding watermark in the lowest frequency band (LL) is simulated. As a simulation result using DWT, the watermarking performance of simultaneously embedding in HH, LH, and HL band of DWT(6 level) is better than that of different cases. The result of AWPT(3 level) improves the correlation value compared to that of DWT(3 level). In addition, insertion the watermark to the LL band about 30~60% of all watermarks improves the correlation value while PSNR performance decreases 1~2dB.

• 한국지구과학회:학술대회논문집
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• 2005.09a
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• pp.400-407
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• 2005

웨이브렛 변환(Wavelet Transform)은 시간영역과 주파수영역에서 동시에 분석이 가능하고 불연속적인 자료를 분석하는데 유리하기 때문에 그동안 영상을 처리하고 분석하는데 널리 이용되어 왔다. Discrete Wavelet Transform(DWT)는 주어진 영상에서 특성 정보는 유지하면서 다른 여러 종류의 계수로 분해 할 수 있게 해주기 때문에, 계수에 임계치를 적용해 고주파 성분을 제거하면 잡음을 줄일 수 있다. Stationary Wavelet Transform(SWT)는 DWT에서 다운샘플링에 의해 발생하는 문제점을 해결하기 위한 변환방법으로 잡음제거에 DWT보다 효과적이라고 알려져 있다. 이 연구에서는 DWT와 SWT에 의한 필터링을 광학영상과 레이더 영상에 적용하여 보고, 기존의 필터링 기법과 그 결과를 비교하였다. 그 결과 SWT에 의한 방법이 경계성분은 보존하면서 잡음을 가장 효과적으로 줄일 수 있었다.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.13-20
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• 2013

A combined DWT and DCT based watermarking technique of low frequency watermarking with weighted correction is proposed. The DWT has excellent spatial localization, frequency spread and multi-resolution characteristics, which are similar to the theoretical models of the human visual system (HVS). The DCT based watermarking techniques offer compression while DWT based watermarking techniques offer scalability. These desirable properties are used in this combined watermarking technique. In the proposed method watermark bits are embedded in the low frequency band of each DCT block of selected DWT sub-band. The weighted correction is also used to improve the imperceptibility. The extracting procedure reverses the embedding operations without the reference of the original image. Compared with the similar approach by DCT based approach and DWT based approach, the experimental results show that the proposed algorithm apparently preserves superiori mage quality and robustness under various attacks such as JPEG compression, cropping, sharping, contrast adjustments and so on.

• Journal of Broadcast Engineering
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• v.6 no.1
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• pp.32-40
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• 2001

In this paper, we propose a DWT (discrete Wavelet Transform) based watermarking method, which can be conveniently Integrated In the up-coming JPEG2770 baseline system. Although Conventional DWT based watermarking techniques insert watermark signal Into wavelet coefficients after the transform, our proposed method embeds a watermark into wavelet coefficients obtained from the ongoing process of lifting for DWT. The proposed method allows us to selectively determine frequency characteristics of the coefficients where the watermark is embedded. so that the Inserted watermark cannot be removed or altered even when the filter-bank for DWT is known. Through the simulation, we show that the proposed method is more secure and more robust against various attacks than conventional DWT barred watermarking techniques.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.224-227
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• 2004

From 1980's, the DWT(Discrete Wavelet Transform) is applied to the data/image processing. Many people use the DWT in remote sensing for diversity purposes and they are satisfied with the wavelet theory. Though the algorithm for wavelet is very diverse, many people use the standard wavelet such as Daubechies D4 wavelet and biorthogonal 9/7 wavelet. We will overview the wavelet theory for discrete form which can be applied to the image processing. First, we will introduce the basic DWT algorithm and review the wavelet algorithm: EZW (Embedded Zerotree Wavelet), SPIHT(Set Partitioning in Hierarchical Trees), Lifting scheme, Curvelet, etc. Finally, we will suggest the properties of wavelet algorithm; and wavelet filter for each image processing in remote sensing.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.105-108
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• 2001

This paper proposed an efficient memory scheduling method (E$^2$M$^2$) by which the real-time image compression using 2-dimensional discrete wavelet transform(2-D DWT) is possible in an FPGA chip. In this paper, we assumed that the 2-D DWT was performed as the Mallat-tree. After the memory mapping method was proved in software, the memory controller was designed for an commercial SDRAM IC.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2262-2264
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• 1998

This paper propose a new method for the discrete signal : Discrete Wavelet Transform(DWT). This paper is a brief introduction to the DWT and applies the DWT coding for the audio data as an example. We can have a number of hint about the compression algorithm of multimedia resources and the high performance of transmission and storage.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.3
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• pp.240-248
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• 2013

In this paper, we propose a super-resolution algorithm using an adaptive weighted interpolation(AWI) and discrete wavelet transform(DWT). In general, super-resolution algorithms for single-image, probability based operations have been used for searching high-frequency components. Consequently, the complexity of the algorithm is increased and it causes the increase of processing time. In the proposed algorithm, we first find high-frequency sub-bands by using DWT. Then we apply an AWI to the obtained high-frequency sub-bands to make them have the same size as the input image. Now, the interpolated high-frequency sub-bands and input image are properly combined and perform the inverse DWT. For the experiments, we use the down-sampled version of the original image($512{\times}512$) as a test image($256{\times}256$). Through experiment, we confirm the improved efficiency of the proposed algorithm comparing with interpolation algorithms and also save the processing time comparing with the probability based algorithms even with the similar performance.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.1-3
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• 2020

본 논문은 주파수 분석 방법 중 하나인 Discrete Wavelet Transform (DWT)을 활용하여 태양광 직렬 아크 사고를 검출하는 방법에 관하여 다룬다. DWT 알고리즘은 주파수 도메인에서도 시간 축 정보를 표현할 수 있어 기존의 Fast Fourier Transform (FFT) 주파수 분석 알고리즘과 차이점이 있으며, 대용량 태양광 시스템의 직렬 아크 사고 검출에 최적화 되도록 DWT 알고리즘의 속도를 향상시켜 태양광 DC 아크 사고 안전규격인 UL1699B의 요구 조건을 만족시켰다. DWT 알고리즘의 경우 TMS320F28033 기반으로 구현 되었으며 대용량 PV 시스템 적용을 위해 로고스키코일을 전류 센서로 사용하였다. 또한, 모의 DC 직렬 아크 발생 회로를 구축하여 제작한 사고 검출기의 성능을 실제 아크 발생 조건에서 검증하였다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.53-63
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• 2006

A discrete wavelet transform(DWT)-based image watermarking algorithm is proposed in this paper, the proposed method decompose the original image into four subsampled images. Subsampled images are transformed by 2 level DWT, respectively. The proposed method embeds the watermark into one of the subsampled DWT images using edge table that represents dege characteristics of the original image. Without an original image, a watermark is extracted through comparison one subsampled DWT image inserted the watermark with the rest of the submapled DWT images. many exiting methodes do not adequately estimate edge regions where intensities are changed abruptly. The proposed method address with an edge table. Also, even if the watermark is embedded into a low frequency area, our method preserves the image quality. The vality of the proposed method is demonstrated through the PSNR test and subjective image quality that human eyes feel.