Blind deconvolution of medical ultrasound images: parametric inverse filtering approach

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.910179The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used

Image Restoration for Remote Sensing: Overview and Toolbox

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS

Reconstruction of long horizontal-path images under anisoplanatic conditions using multiframe blind deconvolution

All optical systems that operate in or through the atmosphere suffer from turbulence induced image blur. Both military and civilian surveillance, gun sighting, and target identification systems are interested in terrestrial imaging over very long horizontal paths, but atmospheric turbulence can blur the resulting images beyond usefulness. This work explores the mean square error (MSE) performance of a multiframe blind deconvolution (MFBD) technique applied under anisoplanatic conditions for both Gaussian and Poisson noise model assumptions. The technique is evaluated for use in reconstructing images of scenes corrupted by turbulence in long horizontal-path imaging scenarios. Performance is evaluated via the reconstruction of a common object from three sets of simulated turbulence degraded imagery representing low, moderate, and severe turbulence conditions. Each set consisted of 1000 simulated turbulence degraded images. The MSE performance of the estimator is evaluated as a function of the number of images, and the number of Zernike polynomial terms used to characterize the point spread function. A Gaussian noise model-based MFBD algorithm reconstructs objects that showed as much as 40% improvement in MSE with as few as 14 frames and 30 Zernike coefficients used in the reconstruction, despite the presence of anisoplanatism in the data. An MFBD algorithm based on the Poisson noise model required a minimum of 50 frames to achieve significant improvement over the average MSE for the data set. Reconstructed objects show as much as 38% improvement in MSE using 175 frames and 30 Zernike coefficients in the reconstruction

커널에 의한 비근접 부분영상과 저차수 영상을 이용한 영상 선명화 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 유석인.Blind image deblurring aims to restore a high-quality image from a blurry image. Blind image deblurring has gained considerable attention in recent years because it involves many challenges in problem formulation, regularization, and optimization. In optimization perspective, blind image deblurring is a severely ill-posed inverse problemtherefore, effective regularizations are required in order to obtain a high-quality latent image from a single blurred one. In this paper, we propose nonlocal regularizations to improve blind image deblurring. First, we propose to use the nonlocal patches selected by similarity weighted by the kernel for the next blur-kernel estimation. Using these kernel-guided nonlocal patches, we impose regularization that nonlocal patches would produce the similar values by convolution. Imposing this regularization improves the kernel estimation. Second, we propose to use a nonlocal low-rank image obtained from the composition of nonlocal similar patches. Using this nonlocal low-rank image, we impose regularization that the latent image is similar to this nonlocal low-rank image. A nonlocal low-rank image contains less noise by its intrinsic property. Imposing this regularization improves the estimation of the latent image with less noise. We evaluated our method quantitatively and qualitatively by comparing several conventional blind deblurring methods. For the quantitative evaluation, we computed the sum of squared error, peak signal-to-noise ratio, and structural similarity index. For blurry images without noise, our method was generally superior to the other methods. Especially, the results of ours were sharper on structures and smoother on flat regions. For blurry and noisy images, our method highly outperformed the conventional methods. Most of other methods could not successfully estimate the blur-kernel, and the image blur was not removed. On the other hand, our method successfully estimate the blur-kernel by overcoming the noise and restored a high-quality of deblurred image with less noise.Chapter 1 Introduction 1 1.1 Formulation of the Blind Image Deblurring 2 1.2 Approach 4 1.2.1 The Use of Kernel-guided Nonlocal Patches 4 1.2.2 The Use of Nonlocal Low-rank Images 5 1.3 Overview 5 Chapter 2 Related Works 6 2.1 Natural Image Prior 7 2.1.1 Scale Mixture of Gaussians 8 2.1.2 Hyper-Laplacian Distribution 8 2.2 Avoiding No-blur Solution 10 2.2.1 Marginalization over Possible Images 11 2.2.2 Normalization of l1 by l2 13 2.2.3 Alternating I and k Approach 15 2.3 Sparse Representation 17 2.4 Using Sharp Edges 19 2.5 Handling Noise 20 Chapter 3 Preliminary: Optimization 24 3.1 Iterative Reweighted Least Squares (IRLS) 25 3.1.1 Least Squared Error Approximation 26 3.1.2 Weighted Least Squared Error Approximation 26 3.1.3 The lp Norm Approximation of Overdetermined System 27 3.1.4 The lp Norm Approximation of Underdetermined System 28 3.2 Optimization using Conjugacy 29 3.2.1 The Conjugate Direction Method 30 3.2.2 The Conjugate Gradient Method 33 3.3 The Singular Value Thresholding Algorithm 36 Chapter 4 Extracting Salient Structures 39 4.1 Structure-Texture Decomposition with Uniform Edge Map 39 4.2 Structure-Texture Decomposition with Adaptive Edge Map 41 4.3 Enhancing Structures and Producing Salient Edges 43 4.4 Analysis on the Method of Extracting Salient Edges 44 Chapter 5 Blind Image Deblurring using Nonlocal Patches 46 5.1 Estimating a Blur-kernel using Kernel-guided Nonlocal Patches 47 5.1.1 Sparse Prior 48 5.1.2 Continuous Prior 48 5.1.3 Nonlocal Prior by Kernel-guided Nonlocal Patches 49 5.2 Estimating an Interim Image using Nonlocal Low-rank Images 52 5.2.1 Nonlocal Low-rank Prior 52 5.3 Multiscale Implementation 55 5.4 Latent Image Estimation 56 Chapter 6 Experimental Results 58 6.1 Images with Ground Truth 61 6.2 Images without Ground Truth 105 6.3 Analysis on Preprocessing using Denoising 111 6.4 Analysis on the Size of Nonlocal Patches 121 6.5 Time Performance 125 Chapter 7 Conclusion 126 Bibliography 129 요약 140Docto

Blind source separation for clutter and noise suppression in ultrasound imaging:review for different applications

Blind source separation (BSS) refers to a number of signal processing techniques that decompose a signal into several 'source' signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review the existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound super-resolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and highlighted the value of a priori domain knowledge to define effective criteria for signal component selection

Separable Inverse Problems, Blind Deconvolution, and Stray Light Correction for Extreme Ultraviolet Solar Images.

The determination of the inputs to a system given noisy output data is known as an inverse problem. When the system is a linear transformation involving unknown side parameters, the problem is called separable. A quintessential separable inverse problem is blind deconvolution: given a blurry image one must determine the sharp image and point spread function (PSF) that were convolved together to form it. This thesis describes a novel optimization approach for general separable inverse problems, a new blind deconvolution method for images corrupted by camera shake, and the first stray light correction for extreme ultraviolet (EUV) solar images from the EUVI/STEREO instruments. We present a generalization of variable elimination methods for separable inverse problems beyond least squares. Existing variable elimination methods require an explicit formula for the optimal value of the linear variables, so they cannot be used in problems with Poisson likelihoods, bound constraints, or other important departures from least squares. To address this limitation, we propose a generalization of variable elimination in which standard optimization methods are modified to behave as though a variable has been eliminated. Computational experiments indicate that this approach can have significant speed and robustness advantages. A new incremental sparse approximation method is proposed for blind deconvolution of images corrupted by camera shake. Unlike current state-of-the-art variational Bayes methods, it is based on simple alternating projected gradient optimization. In experiments on a standard test set, our method is faster than the state-of-the-art and competitive in deblurring performance. Stray light PSFs are determined for the two EUVI instruments, EUVI-A and B, aboard the STEREO mission. The PSFs are modeled using semi-empirical parametric formulas, and their parameters are determined by semiblind deconvolution of EUVI images. The EUVI-B PSFs were determined from lunar transit data, exploiting the fact that the Moon is not a significant EUV source. The EUVI-A PSFs were determined by analysis of simultaneous A/B observations from December 2006, when the instruments had nearly identical lines of sight to the Sun. We provide the first estimates of systematic error in EUV deconvolved images.PHDApplied and Interdisciplinary MathematicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99797/1/shearerp_1.pd

System approach to robust acoustic echo cancellation through semi-blind source separation based on independent component analysis

We live in a dynamic world full of noises and interferences. The conventional acoustic echo cancellation (AEC) framework based on the least mean square (LMS) algorithm by itself lacks the ability to handle many secondary signals that interfere with the adaptive filtering process, e.g., local speech and background noise. In this dissertation, we build a foundation for what we refer to as the system approach to signal enhancement as we focus on the AEC problem. We first propose the residual echo enhancement (REE) technique that utilizes the error recovery nonlinearity (ERN) to "enhances" the filter estimation error prior to the filter adaptation. The single-channel AEC problem can be viewed as a special case of semi-blind source separation (SBSS) where one of the source signals is partially known, i.e., the far-end microphone signal that generates the near-end acoustic echo. SBSS optimized via independent component analysis (ICA) leads to the system combination of the LMS algorithm with the ERN that allows for continuous and stable adaptation even during double talk. Second, we extend the system perspective to the decorrelation problem for AEC, where we show that the REE procedure can be applied effectively in a multi-channel AEC (MCAEC) setting to indirectly assist the recovery of lost AEC performance due to inter-channel correlation, known generally as the "non-uniqueness" problem. We develop a novel, computationally efficient technique of frequency-domain resampling (FDR) that effectively alleviates the non-uniqueness problem directly while introducing minimal distortion to signal quality and statistics. We also apply the system approach to the multi-delay filter (MDF) that suffers from the inter-block correlation problem. Finally, we generalize the MCAEC problem in the SBSS framework and discuss many issues related to the implementation of an SBSS system. We propose a constrained batch-online implementation of SBSS that stabilizes the convergence behavior even in the worst case scenario of a single far-end talker along with the non-uniqueness condition on the far-end mixing system. The proposed techniques are developed from a pragmatic standpoint, motivated by real-world problems in acoustic and audio signal processing. Generalization of the orthogonality principle to the system level of an AEC problem allows us to relate AEC to source separation that seeks to maximize the independence, hence implicitly the orthogonality, not only between the error signal and the far-end signal, but rather, among all signals involved. The system approach, for which the REE paradigm is just one realization, enables the encompassing of many traditional signal enhancement techniques in analytically consistent yet practically effective manner for solving the enhancement problem in a very noisy and disruptive acoustic mixing environment.PhDCommittee Chair: Biing-Hwang Juang; Committee Member: Brani Vidakovic; Committee Member: David V. Anderson; Committee Member: Jeff S. Shamma; Committee Member: Xiaoli M