• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.11 s.116
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• pp.1149-1157
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• 2006

Donoho et al. suggested a wavelet thresholding denoising method based on discrete wavelet transform. This paper proposes an improved denoising method using a new thresholding function based on translation-invariant wavelet for underwater acoustic measurement. The conventional wavelet thresholding denoising method causes Pseudo-Gibbs phenomena near singularities due to the lack of translation-invariant of the wavelet basis. To suppress Pseudo-Gibbs phenomena, a denoising method combining a new thresholding function based on the translation-invariant wavelet transform is proposed in this paper. The new thresholding function is a modified hard-thresholding to each node according to the discriminated threshold so as to reject unknown external noise and white gaussian noise. The experimental results show that the proposed method can effectively eliminate noise, extract characteristic information of radiated noise signals.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.333-338
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• 2006

This paper proposes an improved do-noising method using multi-thresholding function based on translation-invariant (W) wavelet proposed by Donoho et al. for underwater radiated noise measurement. The traditional wavelet thresholding de-noising method causes Pseudo-Gibbs phenomena near singularities due to discrete wavelet transform. In order to suppress Pseudo-Gibbs Phenomena, a do-noising method combining multi-thresholding function with the translation-invariant wavelet transform is proposed in this paper. The multi-thresholding function is a modified soft-thresholding to each node according to the discriminated threshold so as to reject かon external noise and white gaussian noise. It is verified by numerical simulation. And the experimental results are confirmed through sea-trial using multi-single sensors.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.7-12
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• 2003

This paper propose the 1-pass spatially adaptive wavelet thresholding for image denosing. The method of wavelet thresholding for denosing, has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This spatially adaptive thresholding is extended to the overcomplete wavelet expansion, which yields better results than the orthogonal transform. Experiments show that this proposed method does indeed remove noise significantly, especially for large noise power. Experimental results show that the proposed method outperforms level dependent thresholding techniques and is comparable to spatial Wiener filtering method, 2-pass spatially adaptive wavelet thresholding method in matlab.

• Journal of information and communication convergence engineering
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• v.10 no.1
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• pp.45-52
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• 2012

Thedenoising of a natural image corrupted by Gaussian noise is a long established problem in signal or image processing. Today the research is focus on the wavelet domain, especially using the wavelet threshold method. In this paper, a waveletbased image denoising modified adaptive thresholding method is proposed. The proposed method computes thethreshold adaptively based on the scale level and adaptively estimates wavelet coefficients by using a modified thresholding function that considers the dependency between the parent coefficient and child coefficient and the soft thresholding function at different scales. Experimental results show that the proposed method provides high peak signal-to-noise ratio results and preserves the detailed information of the original image well, resulting in a superior quality image.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1465-1476
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• 2008

In recent years wavelet methods have been focused on block shrinkage or thresholding approaches to accounting for the sparseness of the wavelet representation for an unknown function. The block shrinkage or thresholding methods have been developed in both of classical methods and Bayesian methods. In this paper, we propose a Bayesian approach to selecting wavelet basis functions at each resolution level without MCMC procedure. Simulation study and an application are shown.

• Journal of the Korea society of information convergence
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• v.6 no.1
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• pp.1-6
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• 2013

The electrocardiogram(ECG) is widely used for the diagnosis of heart disease recent. In order to correct diagnosis, wavelet and thresholding is studied. In this study, we study hard inverse thresholding that is apply the existing hard thresholding. It apply to hard inverse thresholding on Pan-Tomkins algorism, that was simplified. The results of mit-bih No. 103 ECG signal is detected R peaks was detected unaffected by signal distortion and noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1122-1128
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• 2007

The denoising for image restoration based on the Wavelet Packet Transform with AA(Absolute Average) making-threshold is presented. The wavelet packet transform leads to be better in the part of high frequency than wavelet transform to eliminate noise. And the existing threshold determination is used standard deviation estimated results in increasing the noise and threshold, and damaging an image quality. In addition that is decreased image restoration PSNR by using the same threshold in spite of changing image because of installing a threshold in proportion of noise size. In contrast the AA thresholding method with wavelet packet is adapted by changing image to set up threshold by statistic quantity of resolved image and is avoided an extreme impact. The results on the experiment has improved 10% and 5% over than the denoising based on simple wavelet transform and wavelet packet respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.155-160
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• 2006

This paper presents the hierarchical block matching algorithm using the 4 subbands of the wavelet transformation and the thresholding method. The proposed algorithm improves the PSNR performance of the reconstructed image using the 4 subbands of the wavelet transformation and reduces the computational complexity by thresholding the motion vector. The experimental results of the proposed algorithm for 'Carphone' image and 'Mother and Daughter' image show that if the thresholding value is 0, the computational complexity is increasing up to 16% and the PSNR performance of the reconstructed image improves the 0.16dB in comparison with that of the existing. hierarchical motion estimation algorithm. In addition, as the thresholding value is increasing, the computational complexity reduces up to 8% and the PSNR performance of the reconstructed image is similar.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.163-167
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• 2002

This paper propose the new spatially adaptive wavelet thresholding for image denosing. The method of wavelet thresholding for denosing, has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. Experimental results show that the proposed method outperforms level dependent thresholding techniques and is comparable to spatial Wiener filtering method in matlab.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.156-160
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• 2014

Thresholding in frequency domain is a simple and effective noise reduction technique. Determination of the threshold is critical to the image quality. The optimal threshold minimizing the Mean Square Error (MSE) is chosen adaptively in the wavelet domain; we utilize an equation of the MSE for the soft-thresholded signal and the histogram of wavelet coefficients of the original image and noisy image. The histogram of the original signal is estimated through the deconvolution assuming that the probability density functions (pdfs) of the original signal and the noise are statistically independent. The proposed method is quite general in that it does not assume any prior for the source pdf.