Two-dimensional adaptive block Kalman filtering of SAR imagery

Includes bibliographical references.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 introduced 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

Shift-invariant image reconstruction of speckle-degraded using bispectrum estimation

Coherent speckle noise is modeled as a multiplicative noise process that has a negative exponential probability density function. Using a homomorphic transfor mation, this speckle noise is converted to a signal-independent, additive process. The speckled images are randomly jittered from frame-to-frame against a uniform background to simulate image motion and/or platform jitter. Multiple images are logarithmically transformed and ensemble averaged in the bispectral domain. The bispectrum ignores this image motion so no blurring results from the ensemble averaging. Object Fourier magnitude and phase information are also retained in the bispectrum so that the resultant image can be uniquely reconstructed. This value is then exponentiated to complete the image reconstruc tion process. Since speckle masks the resolution of details in the noisy image and effectively destroys the object structure within the image, it is seen that image reconstruction using bispectrum estimation results in images that regain their object structure. Both one-dimensional and two-dimensional images were tested using separate bispectral signal reconstruction algorithms for each

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method

One-dimensional processing for adaptive image restoration

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1984.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.Includes bibliographical references.by Philip Chan.M.S

One-dimensional processing for adaptive image restoration

Also issued as Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1984.Includes bibliographical references (p. 97-98).Supported in part by the Advanced Research Projects Agency monitered by ONR. N00014-81-K-0742 NR-0490506 Supported in part by the National Science Foundation. ECS80-07102P. Chan

Bispectral reconstruction of speckle-degraded images

The bispectrum of a signal has useful properties such as being zero for a Gaussian random process, retaining both phase and magnitude information of the Fourier transform of a signal, and being insensitive to linear motion. It has found applications in a wide variety of fields. The use of these properties for reducing speckle in coherent imaging systems was investigated. It was found that the bispectrum could be used to restore speckle-degraded images. Coherent speckle noise is modeled as a multiplicative noise process. By using a logarithmic transformation, this speckle noise is converted to a signal independent, additive process which is close to Gaussian when an integrating aperture is used. Bispectral reconstruction of speckle-degraded images is performed on such logarithmically transformed images when we have independent multiple snapshots

Speckle reduction in SAR imagery

Synthetic Aperture Radar (SAR) is a popular tool for airborne and space-borne remote sensing. Inherent to SAR imagery is a type of multiplicative noise known as speckle. There are a number of different approaches which may be taken in order to reduce the amount of speckle noise in SAR imagery. One of the approaches is termed post image formation processing and this is the main concern of this thesis. Background theory relevant to the speckle reduction problem is presented. The physical processes which lead to the formation of speckle are investigated in order to understand the nature of speckle noise. Various statistical properties of speckle noise in different types of SAR images are presented. These include Probability Distribution Functions as well as means and standard deviations. Speckle is considered as a multiplicative noise and a general model is discussed. The last section of this chapter deals with the various approaches to speckle reduction. Chapter three contains a review of the literature pertaining to speckle reduction. Multiple look methods are covered briefly and then the various classes of post image formation processing are reviewed. A number of non-adaptive, adaptive and segmentation-based techniques are reviewed. Other classes of technique which are reviewed include Morphological filtering, Homomorphic processing and Transform domain methods. From this review, insights can be gained as to the advantages and disadvantages of various methods. A number of filtering algorithms which are either promising, or are representative of a class of techniques, are chosen for implementation and analysis