A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage

An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double-l1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes

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

Non-local methods for InSAR parameters estimation

In the thesis work the nonlocal paradigm has been investigated in the framework of Multitemporal SAR Interferometry, e.g. Differential Interferometry, Tomography, etc., and single InSAR pair, e.g. DEM generation. In the former, Adaptive Multi-Looking methods have been developed for the generation of interferometric data-stacks. Following the nonlocal approach, the proposed methods rely only on similar pixels according to a suitable similarity measure that exploits the stack's temporal information. An hybrid approach that jointly uses the nonlocal paradigm and transform domain filtering has been investigated for InSAR pair phase estimation. On the track of the BM3D and SARBM3D algorithms, different approaches to the filtering in the transform domain are investigated. Furthermore, a novel approach to the similarity computation and filtering, based on a relative-topography content of the interferometric phase rather than its absolute value, is proposed

InSAR Deformation Analysis with Distributed Scatterers: A Review Complemented by New Advances

Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique able to measure deformation of the earth’s surface over large areas. InSAR deformation analysis uses two main categories of backscatter: Persistent Scatterers (PS) and Distributed Scatterers (DS). While PS are characterized by a high signal-to-noise ratio and predominantly occur as single pixels, DS possess a medium or low signal-to-noise ratio and can only be exploited if they form homogeneous groups of pixels that are large enough to allow for statistical analysis. Although DS have been used by InSAR since its beginnings for different purposes, new methods developed during the last decade have advanced the field significantly. Preprocessing of DS with spatio-temporal filtering allows today the use of DS in PS algorithms as if they were PS, thereby enlarging spatial coverage and stabilizing algorithms. This review explores the relations between different lines of research and discusses open questions regarding DS preprocessing for deformation analysis. The review is complemented with an experiment that demonstrates that significantly improved results can be achieved for preprocessed DS during parameter estimation if their statistical properties are used

Dual modality optical coherence tomography : Technology development and biomedical applications

Optical coherence tomography (OCT) is a cross-sectional imaging modality that is widely used in clinical ophthalmology and interventional cardiology. It is highly promising for in situ characterization of tumor tissues. OCT has high spatial resolution and high imaging speed to assist clinical decision making in real-time. OCT can be used in both structural imaging and mechanical characterization. Malignant tumor tissue alters morphology. Additionally, structural OCT imaging has limited tissue differentiation capability because of the complex and noisy nature of the OCT signal. Moreover, the contrast of structural OCT signal derived from tissue’s light scattering properties has little chemical specificity. Hence, interrogating additional tissue properties using OCT would improve the outcome of OCT’s clinical applications. In addition to morphological difference, pathological tissue such as cancer breast tissue usually possesses higher stiffness compared to the normal healthy tissue, which indicates a compelling reason for the specific combination of structural OCT imaging with stiffness assessment in the development of dual-modality OCT system for the characterization of the breast cancer diagnosis. This dissertation seeks to integrate the structural OCT imaging and the optical coherence elastography (OCE) for breast cancer tissue characterization. OCE is a functional extension of OCT. OCE measures the mechanical response (deformation, resonant frequency, elastic wave propagation) of biological tissues under external or internal mechanical stimulation and extracts the mechanical properties of tissue related to its pathological and physiological processes. Conventional OCE techniques (i.e., compression, surface acoustic wave, magnetomotive OCE) measure the strain field and the results of OCE measurement are different under different loading conditions. Inconsistency is observed between OCE characterization results from different measurement sessions. Therefore, a robust mechanical characterization is required for force/stress quantification. A quantitative optical coherence elastography (qOCE) that tracks both force and displacement is proposed and developed at NJIT. qOCE instrument is based on a fiber optic probe integrated with a Fabry-Perot force sensor and the miniature probe can be delivered to arbitrary locations within animal or human body. In this dissertation, the principle of qOCE technology is described. Experimental results are acquired to demonstrate the capability of qOCE in characterizing the elasticity of biological tissue. Moreover, a handheld optical instrument is developed to allow in vivo real-time OCE characterization based on an adaptive Doppler analysis algorithm to accurately track the motion of sample under compression. For the development of the dual modality OCT system, the structural OCT images exhibit additive and multiplicative noises that degrade the image quality. To suppress noise in OCT imaging, a noise adaptive wavelet thresholding (NAWT) algorithm is developed to remove the speckle noise in OCT images. NAWT algorithm characterizes the speckle noise in the wavelet domain adaptively and removes the speckle noise while preserving the sample structure. Furthermore, a novel denoising algorithm is also developed that adaptively eliminates the additive noise from the complex OCT using Doppler variation analysis

Feature-preserving image restoration and its application in biological fluorescence microscopy

This thesis presents a new investigation of image restoration and its application to fluorescence cell microscopy. The first part of the work is to develop advanced image denoising algorithms to restore images from noisy observations by using a novel featurepreserving diffusion approach. I have applied these algorithms to different types of images, including biometric, biological and natural images, and demonstrated their superior performance for noise removal and feature preservation, compared to several state of the art methods. In the second part of my work, I explore a novel, simple and inexpensive super-resolution restoration method for quantitative microscopy in cell biology. In this method, a super-resolution image is restored, through an inverse process, by using multiple diffraction-limited (low) resolution observations, which are acquired from conventional microscopes whilst translating the sample parallel to the image plane, so referred to as translation microscopy (TRAM). A key to this new development is the integration of a robust feature detector, developed in the first part, to the inverse process to restore high resolution images well above the diffraction limit in the presence of strong noise. TRAM is a post-image acquisition computational method and can be implemented with any microscope. Experiments show a nearly 7-fold increase in lateral spatial resolution in noisy biological environments, delivering multi-colour image resolution of ~30 nm

Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals

Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar. The proposed methodology exploits the nonlinear mapping capabilities of deep stacked autoencoders in combination with density-based clustering. A comparative experimental evaluation of the approach shows promising results in terms of the proposed methodology’s performance

3D reconstruction and object recognition from 2D SONAR data

Accurate and meaningful representations of the environment are required for autonomy in underwater applications. Thanks to favourable propagation properties in water, acoustic sensors are commonly preferred to video cameras and lasers but do not provide direct 3D information. This thesis addresses the 3D reconstruction of underwater scenes from 2D imaging SONAR data as well as the recognition of objects of interest in the reconstructed scene. We present two 3D reconstruction methods and two model-based object recognition methods. We evaluate our algorithms on multiple scenarios including data gathered by an AUV. We show the ability to reconstruct underwater environments at centimetre-level accuracy using 2D SONARs of any aperture. We demonstrate the recognition of structures of interest on a medium-sized oil-field type environment providing accurate yet low memory footprint semantic world models. We conclude that accurate 3D semantic representations of partially-structured marine environments can be obtained from commonly embedded 2D SONARs, enabling online world modelling, relocalisation and model-based applications