• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.236-237
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• 2019

본 논문에서는 웨이블릿 기반의 영상의 노이즈 제거 알고리즘을 제안한다. 기존 Mallat Tree 방식의 웨이블릿 변환을 응용한다. 먼저, 다중 필터링 방법을 제시한다. 웨이블릿 영역에서 각 부대역의 에너지를 고려하여 웨이블릿 변환의 에너지 집중 특성을 극대화 시킨다. 노이즈 영상에 제안한 다중 필터링을 적용한다. 일반 영상에서 나올 수 없는 에너지 부대역을 찾고 이를 제거하여 노이즈를 제거한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.303-307
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• 2002

Although Synthetic Aperture Radar(SAR) is a very powerful and attractive tool, automatic interpretation of SAR images is extremely difficult because of several reason. Spatially, speckle noise reduction in SAR images is important step to interpret the SAR image at the preprocessing step. The speckle noise in SAR images is modeled to be multiplicative, and therefore, a signal-dependent noise. So, it has deflated many image-denoising algorithms that are based on additive noise model. In this paper, we propose an adaptive wavelet shrinkage method for speckle noise reduction in SAR images by analyzing the high frequency level in detail. We first decompose minutely the high frequency level to analyze the noise level. And then, we determine the weighting threshold value per the level, and layer. Finally, using those weighting threshold, we produce the efficient wavelet shrinkage method. So, this method not only reduces the speckle noise, but also preserves image detail and sharpness.

• Journal of Welding and Joining
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• 제17권6호
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• pp.100-107
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• 1999

In this paper, we deal with the speckle noise reduction and parameter estimation of ultrasonic NDT(non-destructive test) signals obtained during weld inspection of piping. The overall approach consists of three major steps, namely, speckle noise analysis, proposition of wavelet domain AR(autoregressive) model and flaw detection by proposed model parameter. The data are first processed whereby signals obtained using vertical and angle beam transducer. Correlation properties of speckle noise are then analyzed using multiresolution analysis in wavelet domain. The parameter estimation curve obtained using the proposed model is classified a flaw in weld region where is contaminated by severe speckle noise and also clear flaw signal is obtained through CA-CFAR threshold estimator that is a nonlinear post-processing method for removing the noise from reconstructed ultrasonic signal.

• International Journal of Control, Automation, and Systems
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• 제1권3호
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• pp.390-394
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• 2003

To classify the significant wavelet coefficients into edge area and noise area, a morphological clustering filter applied to wavelet shrinkage is introduced. New methods for wavelet shrinkage using morphological clustering filter are used in noise removal, and the performance is evaluated under various noise conditions.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.573-579
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• 2001

Wavelet thresholding is a method for he reduction of noise in image. Wavelet coefficients of image are correlated in local characterization. Thee correlations also appear in he original pixel representation of the image, and they do not follow from the characterizations of the wavelet transform. In this paper, we compare noise-free distributions of Bayes approach to improve the classical threshold algorithm.

• 대한의용생체공학회:의공학회지
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• 제29권2호
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• pp.122-131
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• 2008

For B-mode ultrasound images, we propose an image enhancement algorithm based on a multi-resolution approach, which consists of edge enhancing and noise reducing procedures. Edge enhancement processing is applied sequentially to coarse-to-fine resolution images obtained from wavelet-transformed data. In each resolution, the structural features of each pixel are examined through eigen analysis. Then, if a pixel belongs to an edge region, we perform two-step filtering: that is, directional smoothing is conducted along the tangential direction of the edge to improve continuity and directional sharpening is conducted along the normal direction to enhance the contrast. In addition, speckle noise is alleviated by proper attenuation of the wavelet coefficients of the homogeneous regions at each band. This region-based speckle-reduction scheme is differentiated from other methods that are based on the magnitude statistics of the wavelet coefficients. The proposed algorithm enhances edges regardless of changes in the resolution of an image, and the algorithm efficiently reduces speckle noise without affecting the sharpness of the edge. Hence, compared with existing algorithms, the proposed algorithm considerably improves the subjective image quality without providing any noticeable artifacts.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 제13회 신호처리 합동 학술대회 논문집
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• pp.679-682
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• 2000

In this paper, a MMSE filter in homomorphic wavelet transform domain is proposed for restoring an ultrasonic images corrupted by speckle noise. In order to remove effectively the speckle noise which is a kind of multiplicative noise, speckle noise is transformed into a form of additive noise and then the additive noise is denoised through the MMSE filter in homomorphic wavelet transform domain. The proposed method shows much higher quality in terms of ISNR and subject quality.

• 한국자기공명학회논문지
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• 제4권2호
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• pp.103-115
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• 2000

Wavelet transforms are introduced as a new tool to distinguish real peaks from the noise contaminated NMR data in this paper. New algorithms of two wavelet transforms including Daubechies wavelet transform as a discrete and orthogonal wavelet transform (DWT) and Morlet wavelet transform as a continuous and nonorthogonal wavelet transform(CWT) were developed fer noise elimination. DWT and CWT method were successfully applied to the noise reduction in spectrum. The inevitable distortion of NMR spectral baseline and the imperfection in noise elimination were observed in DWT method while CWT method gives a better baseline ahape and a well noise suppressed spectrum.

• 한국멀티미디어학회논문지
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• 제4권4호
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• pp.306-315
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• 2001

잡음환경에서의 음성인식을 위하여 음성에 부가된 잡음을 제거하는 방법에 있어, 기존의 스펙트럼 차감법은 잡음과 음성을 정확히 구별하기 힘들고 정확한 잡음의 특성을 추정할 수 없는 단점이 있다. 또한 웨이브렛 변환영역에서의 잡음제거 방법은 임계값 적용시 저주파 영역보다는 고주파영역에 상대적으로 더 큰 영향을 미쳐 고주파영역에서 신호의 손실이 발생하는 단점이 있다. 본 논문에서는 스펙트럼 차감법 및 웨이브렛 변환을 이용한 잡음제거 방법의 단점을 개선하기 위하여 연속 웨이브렛 변환 영역에서 웨이브렛 계수의 스케일별 표준편차로 묵음구간과 음성 구간을 판별하여 끝점을 검출 후, 잡음이 섞인 음성신호를 이산 웨이브렛 변화에 의해 3개의 대역으로 분리하여 각각의 대역 내에서 스펙트럼 차감법을 적용시키는 방법을 제안한다. 끝점을 검출하고 대역을 나눔으로써 스펙트럼 차감을 적응할 잡음 신호의 특성을 정확히 추출할 수 있다. 실험을 통하여 제안한 방법이 기존의 스펙트럼 차감법 및 웨이브렛 변환을 이용한 잡음제거 방법보다 신호대 잡음비 및 Itakura-Saito거리 측면에서 향상됨을 확인할 수 있었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.234-237
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• 2000

A number of theoretical researches have been done in recent years on the restoration of images and a variety of algorithms have been developed to implement noise reduction methods. However the blurring effect has not been perfectly overcome in the process of noise reduction. In this paper, we propose a new approach to image restoration that the blurring effect is significantly decreased and the performance of the noise reduction improves by eliminating the noise in the wavelet transform domain in comparison with the conventional noise reduction methods. The proposed algorithm performs much better than the conventional in the subjective image quality and PSNR performance. It is verified through computer simulations,