Geometric filter for speckle reduction

Crimmins, Thomas R.

doi:10.1364/ao.24.001438

Cited by 183 publications

(75 citation statements)

References 4 publications

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“…The earlier BCS/FCS system that is illustrated in Figure 7 (left) already offered improved image enhancement over iterative smoothing techniques, including the median filter (Scollar et al, 1984), adaptive averaging using the sigma filter (Lee, 1983), and geometric filtering (Crimmins, 1985). This BCS/FCS system offered improved image enhancement over the results of these systems iterated for an optimal number of times.…”

Section: Comparison To Prior Systemsmentioning

confidence: 94%

“…(Lee, 1983); (e) Output of three iterations of a geometric filter (Crimmins, 1985); (f) Output of four iterations of a geometric filter.…”

Section: Stage 6: Scale Averagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The number of iterations needed for the best results may depend upon the image being processed. This limitation is illustrated for SAR processing through adaptive averaging with a sigrna filter (Lee, 1983) and a geometric filter (Crimmins, 1985) by Grossberg, Mingolla, and Williamson (1995).Our approach further develops the use of surface contrast enhancement, normalization, and diffusive filling-in between form-sensitive boundary signals as a technique for SAR image enhancement, as described by Grossberg et al (1995). Our algorithm capitalizes on the structure-sensitive operations of a neural model of early visual processing in order to enhance the appearance of scenic surfaces based on information distributed across large regions of the image.…”

mentioning

confidence: 96%

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A neural network for enhancing boundaries and surfaces in synthetic aperture radar images

Mingolla¹,

Ross²,

Grossberg³

1999

Neural Networks

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Section: Comparison To Prior Systemsmentioning

confidence: 94%

“…(Lee, 1983); (e) Output of three iterations of a geometric filter (Crimmins, 1985); (f) Output of four iterations of a geometric filter.…”

Section: Stage 6: Scale Averagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 96%

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A neural network for enhancing boundaries and surfaces in synthetic aperture radar images

Mingolla¹,

Ross²,

Grossberg³

1999

Neural Networks

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“…When N statistically independent intensities are added together, the variance of the sum increases N times and the standard deviation increases ffiffiffiffi ffi N p times but the mean value of the sum increases N times. Thus the coefficient of variation, also called the speckle index [47][48][49], which is the ratio of the standard deviation to the mean, in this case decreases as 1= ffiffiffiffi ffi N p . We experimentally prove this by capturing a number of different statistically independent speckle patterns of the object.…”

Section: Suppression Of Quantization Noisementioning

confidence: 99%

Quantization noise and its reduction in lensless Fourier digital holography

Pandey

Hennelly

2011

Appl. Opt.

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Digital holography is an imaging technique that enables recovery of topographic 3D information about an object under investigation. In digital holography, an interference pattern is recorded on a digital camera. Therefore, quantization of the recorded hologram is an integral part of the imaging process. We study the influence of quantization error in the recorded holograms on the fidelity of both the intensity and phase of the reconstructed image. We limit our analysis to the case of lensless Fourier off-axis digital holograms. We derive a theoretical model to predict the effect of quantization noise and we validate this model using experimental results. Based on this, we also show how the resultant noise in the reconstructed image, as well as the speckle that is inherent in digital holography, can be conveniently suppressed by standard speckle reduction techniques. We show that high-quality images can be obtained from binary holograms when speckle reduction is performed.

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Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images

et al. 2021

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IMPORTANCE Interstitial fibrosis and tubular atrophy (IFTA) is a strong indicator of decline in kidney function and is measured using histopathological assessment of kidney biopsy core. At present, a noninvasive test to assess IFTA is not available. OBJECTIVE To develop and validate a deep learning (DL) algorithm to quantify IFTA from kidney ultrasonography images. DESIGN, SETTING, AND PARTICIPANTS This was a single-center diagnostic study of consecutive patients who underwent native kidney biopsy at John H. Stroger Jr. Hospital of Cook County,

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Geometric filter for speckle reduction

Cited by 183 publications

References 4 publications

A neural network for enhancing boundaries and surfaces in synthetic aperture radar images

A neural network for enhancing boundaries and surfaces in synthetic aperture radar images

Quantization noise and its reduction in lensless Fourier digital holography

Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images

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