ABST M-Type K-Nearest Neighbor (ABSTM-KNN) for Image Denoising

Gallegos-Funes, Francisco J.; Ponomaryov, Volodymyr; Rosa, J. de la

doi:10.1093/ietfec/e88-a.3.798

Cited by 13 publications

(11 citation statements)

References 7 publications

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“…Each a voxel (volume elements) has physical size di dj dk × × physical units (e.g. Recently (Gallegos & Ponomaryov, 2004;Gallegos et al, 2005), we proposed the combined RM (Rank M-type) -estimators for applications in image noise suppression. These estimators use the M-estimator combined with the R-estimator, such as the median or ABST (Ansari-Bradley-Siegel-Tukey) estimator.…”

Section: -D Median M-type L-filtersmentioning

confidence: 99%

“…It was demonstrated that the robust properties of the RM-estimators exceed the robust properties of the base R-and M-estimators for the impulsive and speckle noise suppression (Gallegos & Ponomaryov, 2004). The RM-estimator used in the proposed 3-D filtering scheme is presented in such a form (Gallegos & Ponomaryov, 2004;Gallegos et al, 2005):…”

Section: -D Median M-type L-filtersmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Speckle and Impulsive Noise Suppression in 3-D Ultrasound Imaging

Gallegos‐Funes¹,

de-la-Rosa-Vazquez²,

Rosales³

et al. 2011

Ultrasound Imaging

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Section: -D Median M-type L-filtersmentioning

confidence: 99%

Section: -D Median M-type L-filtersmentioning

confidence: 99%

Real-Time Speckle and Impulsive Noise Suppression in 3-D Ultrasound Imaging

Gallegos‐Funes¹,

de-la-Rosa-Vazquez²,

Rosales³

et al. 2011

Ultrasound Imaging

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“…Equations 11-13 can be also applied in processing of the 2D and 3D data [7,9,[38][39][40][41]. Here, an input sample is formed by pixels in a sliding window (or cube for 3D data) that is usually employed in the image processing.…”

Section: Rm-estimatorsmentioning

confidence: 99%

Real-time 2D–3D filtering using order statistics based algorithms

Ponomaryov

2007

J Real-Time Image Proc

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The paper presents a review of the author's own results obtained in the last several years. Some examples of real-time processing of 2D and 3D images are described. In particular, we discuss the noise model and objective criteria that can be applied to characterize the performance of the processing algorithms. Several proposed algorithms based on RM approach are compared with other known ones, demonstrating the advantages in noise suppressing and preservation of fine image details and edges. A number of 2D and 3D image denoising filters are implemented on DSP, realizing real-time mode in the image processing. The performances of the proposed processing algorithms and the known ones are discussed and evaluated here.

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“…In recent works 18,19 , we proposed the combined RM (Rank M-type) -estimators for applications in image noise suppression. These estimators use the M-estimator combined with the R-estimator, such as the median, Wilcoxon or ABST (Ansari-Bradley-Siegel-Tukey) estimator.…”

Section: Proposed 3-d Rank M-type L Filtersmentioning

confidence: 99%

“…It was demonstrated in [18] that the robust properties of the RMestimators exceed the robust properties of the base R-and M-estimators for the impulsive and speckle noise suppression. The RM-estimator used in the proposed 3-D filtering scheme is presented in such a form [18][19] :…”

Section: Proposed 3-d Rank M-type L Filtersmentioning

confidence: 99%

Real-time speckle and impulsive noise suppression in 3-D imaging based on robust linear combinations of order statistics

Varela-Benitez

Gallegos-Funes

Ponomaryov

2007

Real-Time Image Processing 2007

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This paper presents an approach based on linear combinations of order statistics for speckle and impulsive noise reduction in the 3-D ultrasound images. The proposed technique uses the Rank M-type (RM) estimator and this one is adapted to 3-D image processing applications. The real-time implementation is presented using real clinical ultrasound images by means of use of the DSP TMS320C6711. In addition, the results from known techniques are compared with the proposed method to demonstrate its performance in terms of noise suppression, fine detail preservation, and processing time criteria.

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ABST M-Type K-Nearest Neighbor (ABSTM-KNN) for Image Denoising

Cited by 13 publications

References 7 publications

Real-Time Speckle and Impulsive Noise Suppression in 3-D Ultrasound Imaging

Real-Time Speckle and Impulsive Noise Suppression in 3-D Ultrasound Imaging

Real-time 2D–3D filtering using order statistics based algorithms

Real-time speckle and impulsive noise suppression in 3-D imaging based on robust linear combinations of order statistics

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