• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.302-305
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• 2006

In this paper, we have proposed a novel method using quantization of wavelet coefficients for secret data communication. First, speech signal is partitioned into small time frames and the frames are transformed into frequency domain using a WT(Wavelet Transform). We quantize the wavelet coefficients and embedded secret data into the quantized wavelet coefficients. The destination regard quantization errors of received speech as seceret dat. As most speech watermark techniques have a trade off between noise robustness and speech quality, our method also have. However we solve the problem with a partial quantization and a noise level dependent threshold. In additional, we improve the speech quality with de-noising method using wavelet transform. Since the signal is processed in the wavelet domain, we can easily adapt the de-noising method based on wavelet transform. Simulation results in the various noisy environments show that the proposed method is reliable for secret communication.

• 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.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1079-1082
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• 1999

This paper presents a denoising algorithm that can suppress additive noise components while preserving signal components in the wavelet domain. The algorithm uses the local statistics of wavelet coefficients to attenuate noise components adaptively. Then threshohding operation is followed to reject the residuary noise components in the wavelet coefficients. Simulations are carried out over 1-D signals corrupted by Gaussian noise and the experimental results show the effectiveness of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.18 no.1E
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• pp.13-19
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• 1999

This paper introduces an adaptive wavelet transform based on the lifting scheme, which is applied to signal denoising. The wavelet representation using orthogonal wavelet bases has received widespread attention. Recently the lifting scheme has been developed for the construction of biorthogonal wavelets in the spatial domain. Wavelet transforms are performed through three stages: the first stage or Lazy wavelet splits the data into two subsets, even and odd, the second stage calculates the wavelet coefficients (highpass) as the failure to interpolate or predict the odd set using the even, and the third stage updates the even set using neighboring odd points (wavelet coefficients) to compute the scaling function coefficients (lowpass). In this paper, we adaptively find some of the prediction coefficients for better representation of signals and this customizes wavelet transforms to provide an efficient framework for denoising. Special care has been given to the boundaries, where we design a set of different prediction coefficients to reduce the prediction error.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.412-434
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• 2023

This study discusses the high-order diffusion method in the wavelet domain. It aims to improve the edge protection capability of the high-order diffusion method using wavelet coefficients that can reflect image information. During the first step of the proposed diffusion method, the wavelet packet decomposition is a more refined decomposition method that can extract the texture and structure information of the image at different resolution levels. The high-frequency wavelet coefficients are then used to construct the edge detection function. Subsequently, because accurate wavelet coefficients can more accurately reflect the edges and details of the image information, by introducing the idea of state weight, a scheme for recovering wavelet coefficients is proposed. Finally, the edge detection function is constructed by the module of the wavelet coefficients to guide high-order diffusion, the denoised image is obtained. The experimental results showed that the method presented in this study improves the denoising ability of the high-order diffusion model, and the edge protection index (SSIM) outperforms the main methods, including the block matching and 3D collaborative filtering (BM3D) and the deep learning-based image processing methods. For images with rich textural details, the present method improves the clarity of the obtained images and the completeness of the edges, demonstrating its advantages in denoising and edge protection.

• The Journal of the Acoustical Society of Korea
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• v.19 no.4
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• pp.3-15
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• 2000

In this paper we present a new image denoising filter that can suppress additive noise components while preserving signal components in the wavelet domain. The proposed filter, which we call an adaptive wavelet shrinkage(AWS) filter, is composed of two operators: the wavelet killing operator and the adaptive shrinkage operator. Each operator is selected based on the threshold value which is estimated adaptively by using the local statistics of the wavelet coefficients. In the wavelet killing operation, the small wavelet coefficients below the threshold value are replaced by zero to suppress noise components in the wavelet domain. The adaptive shrinkage operator attenuates noise components from the wavelet components above the threshold value adaptively. The experimental results show that the proposed filter is more effective than the other methods in preserving signal components while suppressing noise.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.10
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• pp.1469-1482
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• 1993

A new image coding technique has been proposed based on the wavelet transform. To achieve lower ceding rates and good qualities in reconstructed images, some of wavelet coefficients were removed by thresholding and quantized in accordance with the sensitivity of the human visual system(HVS). For each block of subimages in wavelet transform domain, block thresholding scheme has been used to remove the unimportant wavelet coefficients according to the frequency characteristic and statistical property of wavelet coefficients. The location information of quantized blocks and removed blocks were encoded using run-length coder which is effective for the exponential distribution. Quantized coefficients were encoded using variable length coder which matches well to their distribution. Simulation results show that the reconstructed images maintain high quality with the low bit rate, below 1.0 bits per pel.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.41-48
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• 2010

In this paper, we present an efficient demosaicking method using the difference between color channels in the wavelet domain. In our method, the low frequency wavelet coefficients are obtained by an edge-directive interpolation using the observed high frequency coefficients. The missing high frequency coefficients are obtained by the estimated low frequency coefficients. In order to reduce artifacts in high frequency domain and to improve visual quality, we update the high frequency coefficient using the color difference rule in the wavelet domain. We simulate our demosaicking method in the wavelet domain and compare our algorithm to the existing demosaicking schemes. Experimental results illustrate that the proposed method can generate enhanced demosaicking results.

• 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.137-144
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• 2005

In this paper, the extraction method of the edge histogram directly using wavelet coefficients in the wavelet domain for JPEG2000 images is proposed. MPEG-7 Edge Histogram Descriptor(EHD) extracts edge histogram in the spacial domain. This algorithm has much multiplication and addition for the edge extraction because it needs the decoding processing. However because the proposed algorithm extracts the edge histogram in the wavelet domain, it doesn't need the decoding processing and it decreases multiplication and addition. The Discrete Wavelet Transform(DWT) is a standard transform in JPEG2000. The proposed algorithm uses Le Gall 5/3 filter in JPEG2000 and odd coefficients in LH2 and HL2 sub-band. The edge direction can be decided to use rate of HL2 and LH2 odd coefficients. According to experiments, there is no difference of the efficiency between EHD and the proposed algorithm And the proposed algorithm is much better than EHD for multiplication and addition in the edge extraction of images.