Adaptive restoration of images with speckle

Kuan, D.; Sawchuk, Alexander A.; Strand, T. C.; Chavel, Pierre

doi:10.1109/tassp.1987.1165131

Cited by 653 publications

(281 citation statements)

References 16 publications

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“…which is the result obtained in [14] and [18]. This result is essentially equivalent to the scalar version of the steady state non-adaptive block Kalman filter in (17e) and (19).…”

Section: Implementatio N and Resultssupporting

confidence: 61%

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Two-dimensional adaptive block Kalman filtering of SAR imagery

Azimi-Sadjadi

Bannour

1991

IEEE Trans. Geosci. Remote Sensing

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Abstract-Speckle effects are commonly observed in synthetic aperture radar (SAR) imagery. In airborne SAR systems the effect of this degradation reduces the accuracy of detection substantially. Thus, the elimination of this noise is an important task in SAR imaging systems. In this paper a new method for speckle noise removal is mtroduced using 2-D adaptive block Kalman filtering (ABKF). The image process is represented by an autoregressive (AR) model with nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which take into account not only the effect of speckles as a multiplicative noise but also those of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as that of the additive noise.

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“…which is the result obtained in [14] and [18]. This result is essentially equivalent to the scalar version of the steady state non-adaptive block Kalman filter in (17e) and (19).…”

Section: Implementatio N and Resultssupporting

confidence: 61%

“…A comparison is made between this 2-D ABKF and the local linear minimum variance estimator (LLMVE) in [13], [14], and [18].…”

Section: Implementatio N and Resultsmentioning

confidence: 99%

Two-dimensional adaptive block Kalman filtering of SAR imagery

Azimi-Sadjadi

Bannour

1991

IEEE Trans. Geosci. Remote Sensing

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“…Conventional image filtering techniques, such as mean and median filtering, other adaptive filtering techniques, like the Lee [30], Kuan [31], Frost [32] or Lee-Sigma [33] techniques, and new versions of these filters [34] have been proposed to reduce speckle noise. Most of them use a defined filter window to estimate the local noise variance (NV) of a speckled image and perform an individual filtering process.…”

Section: Speckle Filteringmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.

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“…For the filtering method based on local statistical properties, adaptive mean filters [12,13] have been proposed. These filters remove speckle noise via local image statistics such as mean and variance, instead of requiring degradation and noise function.…”

Section: Introductionmentioning

confidence: 99%

Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images

2016

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Abstract:Breast ultrasound is an important tool used in the medical treatment and diagnosis of breast tumor. However, noise defined as speckles are generated inevitably. Although the existence of speckle may be beneficial to diagnosis if used by a well-trained observer, it often causes disturbance which negatively affects clinical diagnosis, not only by reducing resolution and contrast of ultrasound images, but also by adding difficulties to recognize tumor region accurately. In this paper, we investigate a number of popular de-speckling algorithms, including filters based on frequency domain, filters based on local statistical properties, filters based on minimum mean square error (MMSE), and filters based on Partial Differential Equation (PDE). Two visual measurement evaluation criteria, i.e., Mean to Variance Ratio (VMR) and Laplace Response of Domain (LRD), are chosen for the performance comparison of those filters in the application of ultrasound breast image filtering. Moreover, the filtering effect is further evaluated with respect to the segmentation accuracy of tumor regions. According to the evaluation results, we conclude that Bilateral Filter (BF) achieves the best visual effect. Although Weickert J Diffusion (WJD) and Total Variation (TV) can also obtain good visual effect and segmentation accuracy, they are very time-consuming.

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Adaptive restoration of images with speckle

Cited by 653 publications

References 16 publications

Two-dimensional adaptive block Kalman filtering of SAR imagery

Two-dimensional adaptive block Kalman filtering of SAR imagery

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images

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