• 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 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.149-153
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• 2004

A Impulse denosing filter design of image for the faster processing time and system compatibility using DSP processor is presented on this paper The system hardware is composed of the stand-alone board with 32 bits DSP processor and vision board for image data acquisition with NTSC CCD camera, and the host computer controls them. The denoising method uses the adaptive median filter. The experiment result is that the system leads to denosing effect as 90% and PSNR 22㏈

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.521-524
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• 1999

In spite of advances in image resolution and film contrast, check screen/film mammography remains one of diagnostic imaging modality where the image interpretation is very difficult. For the enhancement of film mammography, in this paper, dyadic wavelet transform is introduced. An unsharp masking technique is proposed and performed in wavelet domain. In addition, simple nonlinear enhancement and a denosing stage that preserves edges using wavelet shrinkage are computed into this technique. In this paper. we propose a new method for the gain setting of nonlinear enhancement and show result and comparison.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.763-768
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• 2011

Image denoising is a lively research field. Today the researches are focus on the wavelet domain especially using wavelet threshold method. We proposed an adaptive threshold method which considering the characteristic of different sub-band, the method is adaptive to each sub-band. Experiment results show that the proposed method extracts white Gaussian noise from original signals in each step scale and eliminates the noise effectively. In addition, the method also preserves the detail information of the original image, obtaining superior quality image with higher peak signal to noise ratio(PSNR).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.605-608
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• 2007

The denoising for image restoration based on the Wavelet Packet with absolute average threshold is presented. The Existing method 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 absolute average threshold with wavelet packet is adapted by changing image to set up threshold by statistic quantity of resolved image and is avoided an extreme impart. The results on the experiment has improved 10% and 5% over than the denoising based on simple wavelet transform and wavelet packet respectively.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1157-1160
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• 2005

The denoising and reconstruction of color images are increasingly studied in the field of computer vision and image processing. Especially, the denoising and reconstruction of color face images are more difficult than those of natural images because of the structural characteristics of human faces as well as the subtleties of color interactions. In this paper, we propose a denoising method based on PCA reconstruction for removing complex color noises on human faces, which is not easy to remove by using vectorial color filters. The proposed method is composed of the following five steps; training of canonical eigenface space using PCA, automatic extracting of face features using active appearance model, relighing of reconstructed color image using bilateral filter, extraction of noise regions using the variance of training data, and reconstruction using partial information of input images (except the noise regions) and blending of the reconstructed image with the original image. Experimental results show that the proposed denosing method efficiently removes complex color noises on input face images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.12
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• pp.2772-2782
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• 1997

In this paepr, a Wiener filtering method in wavelet domain is proposed for restoring an image corrupted by additive white noise. The proposed method utilizes the characteristics of wavelet transform signals and the local statistics of each subband. When estimating the local statistics in each subband, the size of filter window is varied according to each scale. At this point, the local statistics in each wavelet subband is estimated only by using pixedls which have similar statistical property. Experimental results show that the proposed method has better performance over the conventional Lee filter with a window of fixed size.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.270-277
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• 2010

The BAMS filter is a kind of wavelet shrinkage filter based on the Bayes estimators with no simulation, therefore it can be used for a real time filter. The denoising efficiency of BAMS filter is seriously affected by the estimated noise variance in each wavelet band. To remove noise in signals in existing BAMS filter, the noise variance is estimated by using the quartile of the finest level of details in the wavelet decomposition, and with this variance, the noise of the level is removed. In this paper, to remove the image noise includingodified quartile of the level of detail is proposed. And by these techniques, the image noises of mid and high frequency bands are removed, and the results showed that the increased PSNR of ab the midband noise, the noise variance estimation method using the monotonic transform and the mout 2[dB] and the effectiveness in denosing of low noise deviation images.