This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee (1980, 1981, 1986) and Frost (1982) filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.

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Speckle reducing anisotropic diffusion

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the higher order statistical properties of speckle that are important to the restoration procedure. Various adaptive restoration filters for intensity speckle images are derived based on different model assumptions and a nonstationary image model. These filters respond adaptively to the signal-dependent speckle noise and the nonstationary statistics of the original image.

Adaptive restoration of images with speckle

We present a novel automatic and unsupervised change-detection approach specifically oriented to the analysis of multitemporal single-channel single-polarization synthetic aperture radar (SAR) images. This approach is based on a closed-loop process made up of three main steps: (1) a novel preprocessing based on a controlled adaptive iterative filtering; (2) a comparison between multitemporal images carried out according to a standard log-ratio operator; and (3) a novel approach to the automatic analysis of the log-ratio image for generating the change-detection map. The first step aims at reducing the speckle noise in a controlled way in order to maximize the discrimination capability between changed and unchanged classes. In the second step, the two filtered multitemporal images are compared to generate a log-ratio image that contains explicit information on changed areas. The third step produces the change-detection map according to a thresholding procedure based on a reformulation of the Kittler-Illingworth (KI) threshold selection criterion. In particular, the modified KI criterion is derived under the generalized Gaussian assumption for modeling the distributions of changed and unchanged classes. This parametric model was chosen because it is capable of better fitting the conditional densities of classes in the log-ratio image. In order to control the filtering step and, accordingly, the effects of the filtering process on change-detection accuracy, we propose to identify automatically the optimal number of despeckling filter iterations [Step 1] by analyzing the behavior of the modified KI criterion. This results in a completely automatic and self-consistent change-detection approach that avoids the use of empirical methods for the selection of the best number of filtering iterations. Experiments carried out on two sets of multitemporal images (characterized by different levels of speckle noise) acquired by the European Remote Sensing 2 satellite SAR sensor confirm the effectiveness of the proposed unsupervised approach, which results in change-detection accuracies very similar to those that can be achieved by a manual supervised thresholding.

/pdf/an-unsupervised-approach-based-on-the-generalized-gaussian-1pg97hd0aa.pdf

An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images

Automated breast cancer detection and classification using ultrasound images: A survey

Current speckle filters attempt to restore the radar reflectivity using only the multiplicative speckle noise assumption. The best known filters, namely the Frost, Lee or Kuan niters are adaptive filters based on the local statistics, computed in a fixed square window. In this way, the speckle is reduced as a function of the heterogeneity measured by the local coefficient of variation. When the radar reflectivity undergoes significant variations due to the presence of strong scatterers or structural features (edges or lines) in the processing window, such speckle filtering is less effective. In this paper it is shown that the filtering process can be controlled both by the coefficient of variation and by various geometrical ratio detectors. Through shape adaptive windowing, these detectors allow the use of large window sizes for better speckle reduction while preserving spatial resolution and structural features. The backscattered intensity is modelled as K-distributed within speckled targets and...

Structure detection and statistical adaptive speckle filtering in SAR images

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the higher order statistical properties of speckle that are important to the restoration procedure. Various adaptive restoration filters for intensity speckle images are derived based on different speckle model assumptions and a nonstationary image model. These filters respond adaptively to the signal-dependent speckle noise and the nonstationary statistics of the original image.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Adaptive Restoration Of Images With Speckle

Speckle noise exists in all types of coherent imagery such as synthetic aperture radar, acoustic imagery and laser illuminated imagery. Speckle can be reduced by averaging over several uncorrelated speckle images of the same object when these are available. In this paper, we attempt to reduce speckle noise from a single speckle image by using adaptive digital image restoration techniques. Many speckle noise reduction algorithms assume speckle noise is multiplicative. We model the speckle according the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the statistical properties of speckle. A linear minimum mean-square error filter is derived based on our speckle model and a nonstationary image model. The filter responds adaptively to the signal-dependent speckle noise and the nonstationary mean and variance of the original image. The necessary parameters are estimated from the noisy image. The 2-D recursive implementation of this filter is developed as a fast computation algorithm.

D. T. Kuan

Papers

Adaptive restoration of images with speckle

Adaptive Restoration Of Images With Speckle

Nonstationary 2-D recursive filter for speckle reduction