Topic
Speckle noise
About: Speckle noise is a research topic. Over the lifetime, 8335 publications have been published within this topic receiving 129656 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this article, procedures for defining and verifying a statistical noise model are developed, and two multiplicative noise-smoothing algorithms are pre-sented, which are computationally efficient and have the potential of achieving real-time or near-real-time processing.
Abstract: Speckle appearing in synthetic aperture radar (SAR) images is generated by coherent interference of radar echoes from target scatters. Basically, speckle noise has the nature of a multiplicative noise. In this paper procedures for defining and verifying a statistical noise model are developed, and two multiplicative noise-smoothing algorithms are pre-sented. These two algorithms are computationally efficient and have the potential of achieving real-time or near-real-time processing. Several SEASAT SAR and SIR-B (Shuttle Imaging Radar) images are used for illustration.
557 citations
TL;DR: Results on real images demonstrate that the proposed adaptation of the nonlocal (NL)-means filter for speckle reduction in ultrasound (US) images is able to preserve accurately edges and structural details of the image.
Abstract: In image processing, restoration is expected to improve the qualitative inspection of the image and the performance of quantitative image analysis techniques. In this paper, an adaptation of the nonlocal (NL)-means filter is proposed for speckle reduction in ultrasound (US) images. Originally developed for additive white Gaussian noise, we propose to use a Bayesian framework to derive a NL-means filter adapted to a relevant ultrasound noise model. Quantitative results on synthetic data show the performances of the proposed method compared to well-established and state-of-the-art methods. Results on real images demonstrate that the proposed method is able to preserve accurately edges and structural details of the image.
547 citations
TL;DR: An unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm that exhibited lower error than its preexistences.
Abstract: This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.
508 citations
TL;DR: The bias problem is solved by redefining the sigma range based on the speckle probability density functions, and a target signature preservation technique is developed to mitigate the problems of blurring and depressing strong reflective scatterers.
Abstract: The Lee sigma filter was developed in 1983 based on the simple concept of two-sigma probability, and it was reasonably effective in speckle filtering. However, deficiencies were discovered in producing biased estimation and in blurring and depressing strong reflected targets. The advancement of synthetic aperture radar (SAR) technology with high-resolution data of large dimensions demands better and efficient speckle filtering algorithms. In this paper, we extend and improve the Lee sigma filter by eliminating these deficiencies. The bias problem is solved by redefining the sigma range based on the speckle probability density functions. To mitigate the problems of blurring and depressing strong reflective scatterers, a target signature preservation technique is developed. In addition, we incorporate the minimum-mean-square-error estimator for adaptive speckle reduction. Simulated SAR data are used to quantitatively evaluate the characteristics of this improved sigma filter and to validate its effectiveness. The proposed algorithm is applied to spaceborne and airborne SAR data to demonstrate its overall speckle filtering characteristics as compared with other algorithms. This improved sigma filter remains simple in concept and is computationally efficient but without the deficiencies of the original Lee sigma filter.
508 citations
TL;DR: A comparison is made between three different speckle patterns originated by the same referenceSpeckle pattern, and it is shown that the size of the speckles combined with thesize of the used pixel subset clearly influences the accuracy of the measured displacements.
456 citations