• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.111-118
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• 2004

Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data because of the reasonable performance at the low complexity. The VisuShrink which removes the noise with the universal threshold is one of the techniques. The universal threshold is proportional to the noise deviation and the number of data samples. In general, because the noise deviation is not known, one needs to estimate the deviation for determining the value of the universal threshold. But, only for the finest scale wavelet coefficients, it has been known the way of estimating the noise deviation, so the noise in coarse scales cannot be removed with the VisuShrink. We propose here a new denoising method which removes the noise in each scale except the coarsest scale by Visushrink method. The noise deviation at each band is estimated by the monotonic transform and weighted deviation, the product of estimated noise deviation by the weight, is applied to the universal threshold. By making use of the universal threshold and the Soft-Threshold technique, the noise in each band is removed. The denoising characteristics of the proposed method is compared with that of the traditional VisuShrink and SureShrink method. The result showed that the proposed method is effective in denoising on Gaussian noise and quantization noise.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.26-31
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• 2012

The highest frequency band of wavelet decomposition band is divided into 4 equal subbands and by the minimum power of the subbands and by the monotonic transform, the level adapted threshold is obtained. The adapted threshold is applied to the soft threshold technique to denoise high and middle frequency band noise of image signals. And the results of PSNRs are compared with the results obtained by the VisuShrink technique and by the technique using the monotonic transform and the weight value. The results showed the validity of this technique.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.123-130
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• 2007

It is difficult to analyse images because of multiplicative characteristics of speckle noises in SAR images. In this paper. wavelet transform is proposed for restoring SAR images corrupted by speckle noise. The multiplicative noise is transformed into a form of additive noise and then the additive noise is denoised using wavelet thresholding selections such as VisuShrink, SureShrink, BayesShrink and modified BayesShrink. Experimental results on several test images show that the modified BayesShrink yields significantly superior image quality and better Peak Signal to Noise Ratio(PSNR).

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.281-284
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• 2000

이 논문은 웨이브릿 변환 영역의 백색 가우시안 잡음이 부가된 영상에서 최고 대역에서는 Donoho가 제시한 Visushrink 방법으로 잡음을 제거하고 최저대역을 제외한 나머지 대역들은 Monotonic 변환을 이용한 각 대역의 잡음편차를 추정하고 이를 VisuShrink 경계값에 적용하여 Soft-Threshold 기법으로 영상잡음을 제거하는 방법을 제안하였다. 실험 결과 이 논문에서 제시된 혼합방법에 의한 잡음 제거는 Donoho가 제시한 VisuShrink 방법보다 1㏈ 정도의 잡음제거 개선 효과가 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.127-129
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• 2012

Due to the prevalence of digital camera, multi-media etc. the image is being used in everyday life. However, noise always damages the image and the image denoising technology is important part for improving the image visual quality. There are many existing methods to remove noise such as wiener filter, mean filter and VisuShrink etc. However, they perform not good enough for denoising. Hence, in this paper we proposed a hybrid filter algorithm which consists of wiener filter and modified wavelet based thresholding method using adaptive threshold and thresholding function. The proposed algorithm shows not only better low frequency and high frequency property, but also the outstanding noise suppression and edge preservation properties.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.141-147
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• 2014

The wavelet shrinkage is a technique that reduces the wavelet coefficients to minimize the MSE(Mean Square Error) between the signal and the noisy signal by making use of the threshold determined by the variance of the wavelet coefficients. In this paper, by using the monotonic transform and the power of wavelet subbands, new thresholds applicable to the high and the low frequency wavelet bands are proposed, and the thresholds are applied to the ST(soft-threshold) technique to denoise on image signals with additive Gaussian noise. And the results of PSNRs are compared with the results obtained by the VisuShrink technique and those of [15]. The results shows the validity of this technique.