Refined Filtering of Image Noise Using Local Statistics

Lee, J. S.

doi:10.21236/ada080530

Cited by 198 publications

(252 citation statements)

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“…Adaptive filters adapt themselves to the local texture information surrounding a central pixel in order to calculate a new pixel value. Adaptive filters generally incorporate the Kuan filter, Lee filter, Frost filter, Gamma MAP filters [3], [4], [5]. These filters made obvious their superiority measured up to low pass filters, since they have taken into account the local statistical properties of the image.…”

Section: Introductionmentioning

confidence: 99%

Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance

Sudha¹,

Suresh²,

Sukanesh³

2009

IJCTE

145

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Abstract-In medical image processing, image denoising has become a very essential exercise all through the diagnose. Arbitration between the perpetuation of useful diagnostic information and noise suppression must be treasured in medical images. In general we rely on the intervention of a proficient to control the quality of processed images. In certain cases, for instance in Ultrasound images, the noise can restrain information which is valuable for the general practitioner. Consequently medical images are very inconsistent, and it is crucial to operate case to case. This paper presents a wavelet-based thresholding scheme for noise suppression in ultrasound images. Quantitative and qualitative comparisons of the results obtained by the proposed method with the results achieved from the other speckle noise reduction techniques demonstrate its higher performance for speckle reduction Index Terms-Medical imaging, Speckle noise, Ultrasound images, Wavelet Thresholding.

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Section: Introductionmentioning

confidence: 99%

Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance

Sudha¹,

Suresh²,

Sukanesh³

2009

IJCTE

145

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“…PolSAR data offers efficient and reliable information required to extract biophysical and geophysical parameters about the Earth's surface. The polarimetric information of the target can be expressed by the Sinclair scattering matrix [28]: S = S HH S HV S V H S VV (1) where the elements S HH and S VV produce the power return in the copolarized channels, and the elements S HV and S V H produce the power return in the cross-polarized channels. For a reciprocal target matrix, in the monostatic backscattering case, the Pauli basis vector k for PolSAR can be expressed as:…”

Section: Characteristics Of Polsar Datamentioning

confidence: 99%

“…These filter algorithms have been widely applied to reduce speckle by using a weighted combination of the homogeneous sample and its mean. The refined Lee filter selected the similar pixels to reinforce homogeneity according to eight non-square windows as the templates [1,2]. Similarly, in the improved sigma filter, the two-sigma probability ranges were utilizes to select homogeneous pixels [3][4][5], and the intensity-driven adaptive-neighborhood (IDAN) was proposed by grouping pixels with similar statistical properties [6].…”

Section: Introductionmentioning

confidence: 99%

Feature-Based Nonlocal Polarimetric SAR Filtering

et al. 2017

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Polarimetric synthetic aperture radar (PolSAR) images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance (CV) and Pauli basis (PB) to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.

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“…A fim de reduzir o ruído speckle, a matriz de covariância foi filtrada usando o filtro de Lee, com janela 3x3 pixels (LEE, 1981). Esta escolha foi feita, pois testes realizados com janelas maiores suavizaram excessivamente a cena.…”

Section: Processamento Da Imagem Palsar E Dos Elementos Amostraisunclassified

Atributos polarimétricos de imagem radar na inferência de parâmetros morfológicos de macrófitas

et al. 2012

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O propósito deste trabalho é ajustar um modelo de regressão múltipla em função de atributos de imagens de radar polarimétrico, para estimativa da variável morfológica "volume da haste" de macrófitas encontradas na planície de inundação da Amazônia, no Lago Grande de Monte Alegre (Pará - Brasil). Com esta estimativa, pretende-se avaliar o potencial dos dados polarimétricos do sensor ALOS/PALSAR, destacando a importância da informação de fase das imagens de radar. Para este estudo, foram coletados dados de campo em época próxima à aquisição do dado de radar. Variáveis morfológicas, como altura e diâmetro da haste emergente, de três espécies de macrófitas foram medidas em campo e usadas para derivar o "volume da haste", o qual foi modelado usando atributos da imagem de radar. Dois atributos da decomposição de Freeman-Durden, dois de Touzi e um de Pope apresentaram melhor desempenho na modelagem. Apesar do modelo não ter obtido um elevado coeficiente de determinação (44%), ele apresentou boa capacidade preditiva, já que todos os elementos de validação caíram dentro do intervalo de predição de 95% de confiança. Dentre as cinco variáveis independentes do modelo, quatro foram geradas a partir da informação de fase das imagens, o que reforça a importância dessa informação.

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Refined Filtering of Image Noise Using Local Statistics

Cited by 198 publications

References 0 publications

Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance

Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance

Feature-Based Nonlocal Polarimetric SAR Filtering

Atributos polarimétricos de imagem radar na inferência de parâmetros morfológicos de macrófitas

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