Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles. This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution. Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as: Discriminative Random Fields (DRF) Strong Random Fields (SRF) Spatial-Temporal Models Total Variation Models Learning MRF for Classification (motivation + DRF) Relation to Graphic Models Graph Cuts Belief Propagation Features: Focuses on the application of Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice and MRFs on relational graphs derived from images Examines the problems of parameter estimation and function optimization Includes an extensive list of references This broad-ranging and comprehensive volume is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It has been class-tested and is suitable as a textbook for advanced courses relating to these areas.

Markov Random Field Modeling in Image Analysis

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

/pdf/deformable-medical-image-registration-a-survey-2uee59wp1z.pdf

Deformable Medical Image Registration: A Survey

We address the problem of finding sparse solutions to an underdetermined system of equations when there are multiple measurement vectors having the same, but unknown, sparsity structure. The single measurement sparse solution problem has been extensively studied in the past. Although known to be NP-hard, many single-measurement suboptimal algorithms have been formulated that have found utility in many different applications. Here, we consider in depth the extension of two classes of algorithms-Matching Pursuit (MP) and FOCal Underdetermined System Solver (FOCUSS)-to the multiple measurement case so that they may be used in applications such as neuromagnetic imaging, where multiple measurement vectors are available, and solutions with a common sparsity structure must be computed. Cost functions appropriate to the multiple measurement problem are developed, and algorithms are derived based on their minimization. A simulation study is conducted on a test-case dictionary to show how the utilization of more than one measurement vector improves the performance of the MP and FOCUSS classes of algorithm, and their performances are compared.

http://dsp.ucsd.edu/~kreutz/Publications/cotter2005sparse.pdf

Sparse solutions to linear inverse problems with multiple measurement vectors

The article introduces digital image restoration to the reader who is just beginning in this field, and provides a review and analysis for the reader who may already be well-versed in image restoration. The perspective on the topic is one that comes primarily from work done in the field of signal processing. Thus, many of the techniques and works cited relate to classical signal processing approaches to estimation theory, filtering, and numerical analysis. In particular, the emphasis is placed primarily on digital image restoration algorithms that grow out of an area known as "regularized least squares" methods. It should be noted, however, that digital image restoration is a very broad field, as we discuss, and thus contains many other successful approaches that have been developed from different perspectives, such as optics, astronomy, and medical imaging, just to name a few. In the process of reviewing this topic, we address a number of very important issues in this field that are not typically discussed in the technical literature. 

Digital image restoration

Spectroscopic imaging (SI) techniques combine the ability of NMR spectroscopy to identify and measure biochemical constituents with the ability of MR imaging to localize NMR signals. The basic imaging technique acquires a set of spatial-frequency-domain samples on a regular grid and takes an inverse Fourier transform of the acquired data to obtain the spatial-domain image. Unfortunately, the time required to gather the data while maintaining an adequate signal-to-noise ratio (SNR) limits the number of spatial-frequency-domain samples that can be acquired. In this paper, we use a high-resolution MR scout image to obtain edge locations in the sample imaged with MRSI. MRI discontinuities represent boundaries between different tissue types, and these discontinuities are likely to appear in the spectroscopic image as well. We propose a new model that encourages edge formation in the MRSI image reconstruction wherever MR image edges occur. A major difference between our model and previous methods is that an edge found in the MR image need not be confirmed by the data; smoothing is reduced across the edge if either the MR image or the MRSI data suggests an edge. Simulations and results on in vivo MRSI data are presented that demonstrate the effectiveness of the method.

MR spectroscopic image reconstruction using structural information from anatomical MR images

Optimization of Color Filter Sensitivity Functions for Color Filter Array Based Image Acquisition.

Joint reconstruction of three parameter images — spin magnitude, T 2 ∗ decay and off-resonance field map — is an important technique in some applications of magnetic resonance imaging (MRI). Despite its importance, the reconstruction problem is very challenging due to its nonlinearity and ill-conditioning. The standard conjugate gradient (CG) method with regularization is usually very slow for this problem. In this work, we propose a novel regularized trust region (TR) method that reconstructs the three images by solving a constrained linear sub-problem in each iteration. In particular, this method employs a change-of-variable technique, making the sub-problem linear yet keeping the convergence fast. To solve the sub-problem, we use a penalty method with preconditioned conjugate gradient (PCG) algorithm. Based on a synthesized data set, we demonstrate that our algorithm stably converges even with a poor initial guess, and the convergence rate is much higher than that of the CG algorithm.

A regularized trust region method for joint reconstruction of spin magnitude, T 2 ∗ decay, and off-resonance field map

Image reconstruction from Fourier-domain measurements is a specialized problem within the general area of image reconstruction using prior information. The structure of the equations in Fourier imaging is challenging, since the observation equation matrix is non-sparse in the spatial domain but diagonal in the Fourier domain. Recently, the Bayesian image reconstruction with prior edges (BIRPE) algorithm has been proposed for image reconstruction from Fourier-domain samples using edge information automatically extracted from a high-resolution prior image. In the BIRPE algorithm, the maximum a posteriori (MAP) estimate of the reconstructed image and edge variables involves high-dimensional, non-convex optimization, which can be computationally prohibitive. The BIRPE algorithm performs this optimization by iteratively updating the estimate of the image then updating the estimate of the edge variables. In this paper, we propose two techniques for updating the image based on fixed edge variables one based on iterated conditional modes (ICM) and the other based on Jacobi iteration. ICM is guaranteed to converge, but, depending on the structure of the Fourier-domain samples, can be computationally prohibitive. The Jacobi iteration technique is more computationally efficient but does not always converge. In this paper, we study the convergence properties of the Jacobi iteration technique and its parameter sensitivity.

Bayesian image reconstruction from Fourier-domain samples using prior edge information: convergence and parameter sensitivity

In general, image restoration problems are ill posed and need to be regularized. For applications such as realtime video, fast restorations are also needed to keep up with the frame rate. Restoration based on 2D FFT's provides a fast implementation assuming a constant regularization term over the image. Unfortunately, this assumption creates significant ringing artifacts on edges as well as blurrier edges in the restored image. On the other hand, shift-variant regularization will reduce edge artifacts and provide better quality but it destroys the structure that makes use of the 2D FFT possible, thus no longer have the computational efficiency of the FFT. In this paper, we use a Bayesian approach-maximum a posteriori (MAP) estimation to compute an estimate of the original image given the blurred image. To avoid the smoothing of edges, shift-variant regularization must be used. The Huber-Markov random field model is applied to preserve the discontinuities on edges. For fast minimization of the above model, a new algorithm involving the Sherman-Morrison matrix inversion lemma is 
proposed. 
This results in a restored image with good edge preservation and less computation. Experiments show restored images with sharper edges. Convergence is fast, and the computational speed can be improved considerably by breaking the image into subimages.

Fast Huber-Markov edge-preserving image restoration

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