Alessandro Lapini

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 methods.

Fast MAP Despeckling Based on Laplacian–Gaussian Modeling of Wavelet Coefficients

The undecimated wavelet transform and the maximum a posteriori probability (MAP) criterion have been applied to the problem of synthetic-aperture-radar image despeckling. The MAP solution is based on the assumption that wavelet coefficients have a known distribution. In previous works, the generalized Gaussian (GG) function has been successfully employed. Furthermore, despeckling methods can be improved by using a classification of wavelet coefficients according to their texture energy. A major drawback of using the GG distribution is the high computational cost since the MAP solution can be found only numerically. In this letter, a new modeling of the statistics of wavelet coefficients is proposed. Observations of the estimated GG shape parameters relative to the reflectivity and to the speckle noise suggest that their distributions can be approximated as a Laplacian and a Gaussian function, respectively. Under these hypotheses, a closed form solution of the MAP estimation problem can be achieved. As for the GG case, classification of wavelet coefficients according to their texture content may be exploited also in the proposed method. Experimental results show that the fast MAP estimator based on the Laplacian-Gaussian assumption and on the classification of coefficients reaches almost the same performances as the GG version in terms of speckle removal, with a gain in computational cost of about one order of magnitude.

Blind Speckle Decorrelation for SAR Image Despeckling

In the past few decades, several methods have been developed for despeckling synthetic aperture radar (SAR) images. A considerable number of them have been derived under the assumption of a fully-developed speckle model in which the multiplicative speckle noise is supposed to be a white process. Unfortunately, the transfer function of SAR acquisition systems can introduce a statistical correlation, which decreases the despeckling efficiency of such filters. In this paper, a whitening method is proposed for processing a complex image acquired by a SAR system. We demonstrate that the proposed approach allows the successful application of classical despeckling algorithms. First, we perform an estimation of the SAR system frequency response based on some statistical properties of the acquired image and by using realistic assumptions. Then, a decorrelation process is applied on the acquired image, taking into account the presence of point targets. Finally, the image is despeckled. The experimental results show that the despeckling filters achieve better performance when they are preceded by the proposed whitening method; furthermore, the radiometric characteristics of the image are preserved.

Amplitude vs intensity Bayesian despeckling in the wavelet domain for SAR images

In this paper, the problem of despeckling SAR images when the input data is either an intensity or an amplitude signal is revisited. State-of-the-art despeckling methods based on Bayesian estimators in the wavelet domain, recently proposed in the literature, are taken into consideration. First, how these methods proposed for one format (e.g., intensity) can be adapted to the other format (e.g., amplitude) is investigated. Second, the performance of such algorithms in both cases is analyzed. Experimental results carried out on simulated speckled images and on true SAR data are presented and discussed in order to assess the best strategy. From these results, it can be observed that filtering in the amplitude domain yields better performances in terms of objective quality indexes, such as preservation of structural details, as well as in terms of visual inspection of the filtered SAR data.

Comparison of super-resolution methods for quality enhancement of digital biomedical images

The problem of resolution enhancement has been recently attracted the image processing community both for its theoretical and applications relevance. Achieving an higher and higher resolution capability is the objective of imaging sensor technology, which is often paid in terms of high equipment costs. On the other hand, the advances in signal processing theory and equipment make solutions for resolution enhancement based on post-processing of low-resolutions acquisitions appealing. Some type of biomedical imaging systems, such as computer tomography or magnetic resonance, are specific examples that can benefit from super-resolution of images. In this paper, we review some advanced techniques available for single image super-resolution and propose a variation of one method based on sparse representations. Then, we compare the performance of each method when they are applied to the quality enhancement of low-resolution biomedical images.

Bayesian despeckling of SAR images based on Laplacian-Gaussian modeling of undecimatedwavelet coefficients

The undecimated wavelet transform and the maximum a posteriori (MAP) criterion have been applied to the problem of despeckling SAR images. The solution is based on the assumption that the wavelet coefficients have a known distribution. In previous works, the generalized Gaussian function has been successfully employed. In this case, a major problem is the computational cost, since the solution can be found only numerically. In this work, a different modeling is proposed. The observation of the experimental histograms of the wavelet coefficients related to the reflectivity and to speckle noise demonstrates that their distributions can be approximated as a Laplacian and a Gaussian function, respectively. Under these hypotheses, a closed form solution of the MAP estimation problem can be achieved. In addition, a closed form estimator based on the MMSE criterion also exists. The experimental results show that the fast MAP and MMSE estimators reach almost the same performances of their generalized Gaussian based counterparts in terms of speckle removal, with a computational gain of about one order of magnitude.

Design of active noise control systems for pulse noise

Active noise control (ANC) methods have been successfully studied and tested for the cancellation of stationary noise. In the last decade, some adaptive solutions for the case of impulsive noise have been proposed in the literature. Nevertheless, such a model fits a limited class of impulsive disturbances that characterize practical scenarios. In this paper a preliminary study on the design of a non-adaptive deterministic ANC system for pulse signals that relies on no statistical assumptions is developed. The spatial audio rendering framework of Wave Field Synthesis is formally adopted in order to synthesize the cancelling sound field by means of an array of secondary sources. A set of preliminary simulations in free field environment, as well as the impact of array geometry and extension, has been carried out in view of forthcoming geometry and shape optimization of the system.

Reverse Engineering of Mechanical Parts: a Template-Based Approach

Abstract Template-Based reverse engineering approaches represent a relatively poorly explored strategy in the field of CAD reconstruction from polygonal models. Inspired by recent works suggesting the possibility/opportunity of exploiting a parametric description (i.e. CAD template) of the object to be reconstructed in order to retrieve a meaningful digital representation, a novel reverse engineering approach for the reconstruction of CAD models starting from 3D mesh data is proposed. The reconstruction process is performed relying on a CAD template, whose feature tree and geometric constraints are defined according to the a priori information on the physical object. The CAD template is fitted upon the mesh data, optimizing its dimensional parameters and positioning/orientation by means of a particle swarm optimization algorithm. As a result, a parametric CAD model that perfectly fulfils the imposed geometric relations is produced and a feature tree, defining an associative modelling history, is available to the reverse engineer. The proposed implementation exploits a cooperation between a CAD software package (Siemens NX) and a numerical software environment (MATLAB). Five reconstruction tests, covering both synthetic and real-scanned mesh data, are presented and discussed in the manuscript; the results are finally compared with models generated by state of the art reverse engineering software and key aspects to be addressed in future work are hinted at.

Active noise control for pulse signals by wave field synthesis

Active noise control (ANC) techniques have been intensively studied and applied for the cancellation of stationary noise. More recently, adaptive solutions for the case of impulsive noise, i.e. stochastic processes for which statistical moments superior to the first are not defined, have been proposed in the literature. Nevertheless, such a model fits a limited class of impulsive disturbances that could be experienced in practice. This paper introduces a preliminary study on a non-adaptive deterministic ANC technique for pulse signals that relies on no statistical assumptions. In particular, the spatial audio rendering framework of Wave Field Synthesis is formally adopted in order to synthesize the cancelling acoustic field. Simulations in free field environment, including the analysis of impairments such as time mismatch and template mismatch, have been carried out, showing promising performances in terms of noise cancellation.

An experimental setup for multiresolution despeckling of COSMO-SkyMed image products

This paper describes the most recent achievements in speckle reduction of COSMO-SkyMed (CSK ®) synthetic aperture radar (SAR) data. An advanced multresolution despeckling filter, based on undecimated wavelet transform (UDWT) and maximum a-posteriori (MAP) estimation has been specialized and optimized to CSK ® data, both single- and multilook. The tradeoff between performances and computational complexity has been investigated: Laplacian-Gaussian and generalized Gaussian (GG) priors for MAP estimation in UDWT domain differ by one order of magnitude in computation cost. The former are more complex but yield the best results attainable with a Bayesian estimation carried out in the UDWT domain. Pre-processing of point targets and segmentation of wavelet planes has been exploited to effectively handle the heterogeneity of the data. The effects of multilooking have been investigated. Starting from single-look complex (SLC) data, the spatial correlation coefficients (CC) of speckle and the equivalent number of looks (ENL) of all products have been theoretically calculated. It is proven that, besides having an inherently better radiometric quality, multilooked products exhibit a lower spatial correlation of speckle than single-look products, thereby better falling under the assumption of uncorrelated speckle, exploited by the majority of model-based despeckling filters, included those used in the present work. The effects of spatial resampling have been investigated as well. Unlike MAP filters in spatial domain (e.g. the Gamma-MAP filter), MAP filters in wavelet domain are little sensitive to resampling, because the fundamental hypotheses on which they rely are not violated because of resampling. Comparisons with the state of the art are also provided and shown to be more than favorable. Besides traditional supervised methods to evaluate the quality of despeckling, a novel procedure, fully automated, based on bivariate analysis of noisy and denoised image has been devised. Its results agree both with visual analysis and with manual measurements.