Digital image smoothing and the sigma filter

Lee, Jong-Sen

doi:10.1016/0734-189x(83)90047-6

Cited by 735 publications

(332 citation statements)

References 11 publications

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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%

“…Another filter that has appeared in the last decade is the improved sigma filter [47], which was developed as an improvement to the previous Lee sigma filter implemented by the same author in 1983 [33]. The Lee sigma filter, based on the concept of two-sigma probability, had deficiencies dealing with biased estimation and blurring and depressing strong reflected targets [47], which were more exposed with the advances that SAR technology has experienced in the last two decades.…”

Section: Speckle Filteringmentioning

confidence: 99%

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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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Section: Speckle Filteringmentioning

confidence: 99%

Section: Speckle Filteringmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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“…(1) 3D sigma ÿltering, to suppress noise while preserving thin structures [13], (2) 3D symmetric region growing segmentation, to extract the desired raw tree [14], (3) 3D cavity deletion, to ÿll possible 3D holes inside the tree branches and produce a solid tree. (4) 3D thinning, to deÿne the skeleton of the tree while preserving homotopy [15], (5) tree representation, to convert the image-form tree skeleton data into a compact graph-like data structure [16], (6) root identiÿcation, to help put the tree's branching geometry in proper hierarchical order.…”

Section: Tree Extractionmentioning

confidence: 99%

Multi-generational analysis and visualization of the vascular tree in 3D micro-CT images

Wan

Ritman

Higgins

2002

Computers in Biology and Medicine

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Micro-CT scanners can generate large high-resolution three-dimensional (3D) digital images of small-animal organs, such as rat hearts. Such images enable studies of basic physiologic questions on coronary branching geometry and uid transport. Performing such an analysis requires three steps: (1) extract the arterial tree from the image; (2) compute quantitative geometric data from the extracted tree; and (3) perform a numerical analysis of the computed data. Because a typical coronary arterial tree consists of hundreds of branches and many generations, it is impractical to perform such an integrated study manually. An automatic method exists for performing step (1), extracting the tree, but little e ort has been made on the other two steps. We propose an environment for performing a complete study. Quantitative measures for arterial-lumen cross-sectional area, inter-branch segment length, branch surface area and others at the generation, inter-branch, and intra-branch levels are computed. A human user can then work with the quantitative data in an interactive visualization system. The system provides various forms of viewing and permits interactive tree editing for "on the y" correction of the quantitative data. We illustrate the methodology for 3D micro-CT rat heart images. ?

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“…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]. Furthermore, the bilateral filter calculates the weighted average based on the similarity between pixels in the local windows of …”

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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Digital image smoothing and the sigma filter

Cited by 735 publications

References 11 publications

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Multi-generational analysis and visualization of the vascular tree in 3D micro-CT images

Feature-Based Nonlocal Polarimetric SAR Filtering

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