nils-herrmann/clean_scirepeval huggingface dataset:
Efficient multiscale regularization with applications to the computation of optical flow

A new approach to regularization methods for image processing is introduced and developed using as a vehicle the problem of computing dense optical flow fields in an image sequence. The solution of the new problem formulation is computed with an efficient multiscale algorithm. Experiments on several image sequences demonstrate the substantial computational savings that can be achieved due to the fact that the algorithm is noniterative and in fact has a per pixel computational complexity that is independent of image size. The new approach also has a number of other important advantages. Specifically, multiresolution flow field estimates are available, allowing great flexibility in dealing with the tradeoff between resolution and accuracy. Multiscale error covariance information is also available, which is of considerable use in assessing the accuracy of the estimates. In particular, these error statistics can be used as the basis for a rational procedure for determining the spatially-varying optimal reconstruction resolution. Furthermore, if there are compelling reasons to insist upon a standard smoothness constraint, the new algorithm provides an excellent initialization for the iterative algorithms associated with the smoothness constraint problem formulation. Finally, the usefulness of the approach should extend to a wide variety of ill-posed inverse problems in which variational techniques seeking a "smooth" solution are generally used.
Analysis and mitigation of calcium artifacts in cardiac multidetector CT

Multi-detector computed tomography offers the promise of a non-invasive alternative to invasive coronary angiography for the evaluation of coronary artery disease. An impediment preventing its widespread adoption is the presence of image "blooming" artifacts due to the presence vascular calcium. This blooming has been linked to cardiac motion, beam hardening, and resolution effects. In this paper we study the contribution of these elements to blooming in a controlled way and conclude that the strongest effect for current systems is due to resolution. We then present a multi- component algebraic-type reconstruction approach to mitigate such blooming artifacts, motivated by recent results in image inpainting. The reconstruction approach decomposes the image into a collection of spatially localized components, each with a set of homogeneous properties. The local nature of the decomposition and constraints prevents artifacts from contaminating other image regions.
Superresolution and edge-preserving reconstruction of complex-valued synthetic aperture radar images

We propose a regularization-based method for the complex-valued synthetic aperture radar (SAR) image formation problem. The method can produce images with higher resolution than that supported by the measured data, as well as images with reduced variability of reflectivity magnitudes within homogeneous regions and preserved region boundaries. This is achieved by the inclusion of prior information in the regularized data inversion process through nonquadratic potential functions. For this task, we demonstrate the use of a variety of potential functions in our framework. The technique effectively deals with the random-phase nature of the underlying SAR reflectivities. For an efficient numerical solution, we extend half-quadratic regularization methods to this complex-valued problem. We demonstrate the performance of the method on real SAR data.
A fast level set method without solving PDEs [image segmentation applications]

In this paper, we propose a novel and fast level set method without the need for solving PDEs (partial differential equations) while preserving the advantages of level set methods, such as the automatic handling of topological changes. The foundation of our method is the direct use of an optimality condition for the final curve location based on the speed field. By testing this condition, only simple operations like insertion and deletion on two lists of boundary points are needed to evolve the curve. Our method is suitable for a set of general evolution speeds that are composed of two parts: an external speed derived from the image data and a speed term imposing boundary smoothness or regularization. In our experiments, we demonstrate that our algorithm is approximately two orders of magnitude faster than previous optimized narrow band algorithms for image segmentation tasks.
Applications of rank-based median filters in power electronics

A class of robust, rank-based signal processing filters is considered, particularly with regard to its usefulness in the power electronics field. This class of nonlinear filters is characterized by the inclusion of a sorting element in the signal path. The sorting operation allows these filters to suppress impulsive noise while preserving edges and monotonic sections of signals. This introductory work concentrates primarily on the median filter, it being the most accessible filter of the class. A working knowledge of issues arising in design and implementation is developed.
High-resolution biosensor spectral peak shift estimation

In this work we present a maximum likelihood (ML) approach to high-resolution estimation of the shifts of a spectral signal. This spectral signal arises in application of optically-based resonant biosensors, where high-resolution in the estimation of signal shift is synonymous with high-sensitivity to biological interactions. The underlying signal is nonuniformly sampled and exhibits Poisson noise statistics. Shift estimation accuracies orders of magnitude finer than the sample spacing are sought. The ML-based formulation leads to a solution approach different from typical resonance shift estimation methods based on polynomial fitting and peak (or null) estimation and tracking.
A method for choosing the regularization parameter in generalized Tikhonov regularized linear inverse problems

This paper presents a systematic and computable method for choosing the regularization parameter appearing in Tikhonov-type regularization based on non-quadratic regularizers. First, we extend the notion of the L-curve, originally defined for quadratically regularized problems, to the case of non-quadratic functions. We then associate the optimal value of the regularization parameter for these non-quadratic problems with the corner of the resulting generalized L-curve. We identify the corner of this L-curve as the point of tangency between a straight line of arbitrary slope and the L-curve. This definition results in a corresponding algebraic equation which the optimal regularization parameter must satisfy. This algebraic equation naturally leads to an iterative algorithm for the optimal value of the regularization parameter. The convergence of this iterative algorithm is established. Simulation results confirm that the proposed method yields values of the regularization parameters that result in good reconstructions for non-quadratic problems.
Moment-based geometric image reconstruction

In this paper, we discuss two interesting instantiations of the moment problem in image processing. The first involves the estimation of moments of an image indirectly from projections, and the reconstruction of the image from these moments. The second relates the reconstruction of binary polygons from moments to well-known algorithm in array signal processing. Through these examples, we place the moment problem into a geometric perspective and illustrate how this perspective leads to a number of interesting practical applications in image processing and other fields.<<ETX>>
Shape reconstruction from unorganized points with a data-driven level set method

We propose a new method for shape reconstruction from noisy and unorganized point data. We represent a shape through its signed distance function and formulate shape reconstruction as a constrained energy minimization problem directly based on the observed point set. The associated energy function includes both the likelihood of the observed data points and a smoothness prior on the reconstructed shape. To solve this optimization problem, an efficient data-driven level set method is developed. Our method is robust to local minima, clutter, and noise. It is also applicable to situations where the data are sparse. The topological nature of the underlying shape is handled automatically through the level set formalism.
Wavelet-based multiscale stochastic models for efficient tomographic discrimination of fractal fields

Proposes a technique for discrimination of fractal fields with different fractal dimensions, directly from the noisy and sparse tomographic projection data. This application is motivated from the medical field, where a change in fractal dimension is used to differentiate between normal and abnormal conditions in many different contexts, including diagnosis of liver abnormalities. The conventional method for discrimination of fractal fields from tomographic data is based on the calculation of the slope of the power spectra of the corresponding projections. This method, derived from the Radon transform results, breaks down in case the projection data are sparse and/or noisy. In order to avoid any restrictions on the duality and quantity of the projection data, we formulate our discrimination problem in a discrete hypothesis testing framework, the solution to which is given by the maximum-log-likelihood discrimination rule. The problem of discriminating fractal fields through likelihood calculations is, however, complicated by the fact that inverses and determinants of large, full, and generally ill conditioned fractal-field data covariance matrices are required. We show that these complications in the likelihood calculations can be removed by a transformation to the multiscale framework. The multiscale data covariance matrices are sparse and in addition, are naturally partitioned into ill conditioned coarsest scale approximation blocks and relatively well conditioned multiscale detail blocks. We simplify our likelihood calculations by using the class of multiscale stochastic models defined on trees to realize accurate approximations of the detail block of the data covariance matrices.<<ETX>>
A new multiresolution algorithm for image segmentation

We present here a novel multiresolution-based image segmentation algorithm. The proposed method extends and improves the Gaussian mixture model (GMM) paradigm by incorporating a multiscale correlation model of pixel dependence into the standard approach. In particular, the standard GMM is modified by introducing a multiscale neighborhood clique that incorporates the correlation between pixels in space and scale. We modify the log likelihood function of the image field by a penalization term that is derived from a multiscale neighborhood clique. Maximum likelihood (ML) estimation via the expectation-maximization (EM) algorithm is used to estimate the parameters of the new model. Then, utilizing the parameter estimates, the image field is segmented with a MAP classifier. It is demonstrated that the proposed algorithm provides superior segmentations of synthetic images, yet is computationally efficient.
Time-domain reconstruction of moderately rough dielectric interfaces via quasi-ray Gaussian beams

The problem of determining the properties of rough surfaces from electromagnetic scattered field data arises in many importance subsurface sensing problems such as underground imaging using data from a ground penetrating radar (GPR). In GPR systems, arrays of above-ground transmitters and receivers illuminate areas of interest and receive backscattered signals from shallowly buried objects from surface reflections. The shape of the air-ground interface is unknown, and it is a principal corruptor of the backscattered signal from subsurface targets of interest. In order to enhance subsequent detection, classification and inverse scattering processing, it is important to compensate for the distortion introduced by the air-ground interface. As a first step toward this goal, we previously addressed the problem of estimating the profile of one-dimensional (1D) moderately rough dielectric interfaces from a limited number of multifrequency measurements under time-harmonic excitation. In this paper, we extend the approach to the case of time-dependent (short pulse) excitation. The proposed extension is based on a developed Gabor-based narrrow-waisted pulsed-beam representation of short-pulse scattering by moderately rough surfaces.
Differentiable minimin shape distance for incorporating topological priors in biomedical imaging

In the application of curve evolution and level set methods to biomedical image analysis, the incorporation of geometric priors for isolated shapes has been proved useful. On the other hand, the inclusion of a priori topological information concerning the relationship of multiple shapes remains a challenge. In this paper, we propose a differentiable minimin shape distance (DMSD) that is indicative of the topological relation between shapes. A curve evolution equation based on its first variation is derived and this enables us to incorporate this prior into a curve evolution framework. We demonstrate the application of the DMSD by proposing an extension to the Chan-Vese image segmentation model to incorporate topological prior information for challenging image segmentation tasks.
Multiscale representations of Markov random fields

A framework for multiscale stochastic modeling was introduced (K.C. Chou et al., 1989) based on coarse-to-fine scale-recursive dynamics defined on trees. This model class has some attractive characteristics which lead to extremely efficient, statistically optimal signal and image processing algorithms. In the present work, the authors describe how 1-D Markov processes and 2-D Markov random fields (MRFs) can be represented within this framework. In addition, they propose a framework for reduced-order multiscale modeling of Gaussian MRFs.<<ETX>>
A learning-based approach to explosives detection using Multi-Energy X-Ray Computed Tomography

In this paper we consider the task of classifying materials into explosives and non-explosives according to features obtainable from Multi-Energy X-ray Computed Tomography (MECT) measurements. The discriminative ability of MECT derives from its sensitivity to the attenuation versus energy curves of materials. Thus we focus on the fundamental information available in these curves and features extracted from them. We study the dimensionality and span of these curves for a set of explosive and non-explosive compounds and show that their space is larger than two-dimensional, as is typically assumed. In addition, we build support vector machine classifiers with different feature sets and find superior classification performance when using more than two features and when using features different than the standard photoelectric and Compton coefficients. These results suggest the potential for improved detection performance relative to conventional dual-energy X-ray systems.
Performance guarantees in sensor networks

The sensor network for monitoring distributed spatial phenomena has emerged as an area of significant practical interest. In this paper we investigate fundamental issues in detection of spatially distributed phenomena under communication constraints. The novelty of the paper is in providing a tradeoff between global performance and costs involved in communication. In particular we focus our attention on boundary estimation and develop a framework to optimize communication costs subject to worst-case misclassification guarantees. It is shown that the communication cost is primarily a function of two parameters: (1) length of the boundary; (2) overall misclassification error - which leads us to the conclusion that wireless sensor network performance is comparable to that obtained with a wired network of sensors.
Spatio-temporal deconvolution of perfusion CT data in rectal tumor patients

Anatomical imaging modalities such as MR and CT produce high resolution images that are essential in modern health care. Anatomical imaging, however, can be supplemented with functional imaging to provide additional insights into the status and progression of disease. In perfusion imaging, a bolus of contrast entering a tissue region of interest is carefully monitored in order to derive hemodynamic parameters. While useful, perfusion parameter maps tend to be noisy, in part because the parameters of every voxel are computed independently of its neighboring voxels. A spatio-temporally correlated approach to estimating perfusion parameters has been previously introduced. In this paper, we expand upon this earlier formulation and demonstrate its functionality on a set of rectal tumor patients undergoing CT perfusion (CTp) studies. Our results show that our spatio-temporal deconvolution approach is less noisy than conventional singular value deconvolution approach, and show greater separation between normal and cancerous regions.
Enhanced, high resolution radar imaging based on robust regularization

We propose an enhanced image reconstruction method for spotlight-mode synthetic aperture radar (SAR). Our approach involves extension of feature preserving regularization techniques developed in other applications to the complex-valued SAR imaging problem. Compared to conventional image formation schemes, our approach offers increased resolvability of point-scatterers, enhancement of object shapes, reduced sidelobes and reduced speckle. We present the effectiveness of the proposed method on synthetic and real SAR scenes.
Multiscale geometrical feature extraction and object recognition with wavelets and morphology

In this work, a novel method of multiscale geometric feature extraction and object recognition is developed. In particular, the new representation should have the following characteristics. First, the coarse scale features should have a geometric interpretation so that the overall geometry of the object is discernible from just these features. Second, the presence of fine scale detail should not change the coarse scale representation. These two goals are not achieved by current techniques which are based on error as measured by the L/sup 2/ norm. Two methods to accomplish these goals are presented. In the first, morphological filtering and wavelet networks are used. In the second, the correlation criteria of the matching pursuit algorithm of Mallat and Zhang (1993) is modified to obtain a variable, high resolution matching pursuit.
Multiscale segmentation and anomaly enhancement of SAR imagery

We present an efficient multiscale approach to the segmentation of natural clutter, specifically grass and forest, in synthetic aperture imagery (SAR) and to the enhancement of anomalous image regions therein. The methods we propose exploit the coherent nature of SAR sensors. In particular, they characterize the scale-to-scale statistical differences in imagery of various terrain categories due to radar speckle. To achieve this, we employ a recently introduced class of multiscale stochastic processes that provide a powerful framework for describing random processes and fields that evolve in scale. We build models representative of each relevant category of terrain and use them to direct subsequent decisions on pixel classification, segmentation, and anomaly presence.
Multiresolution stochastic imaging of satellite oceanographic altimetric data

Large data assimilation problems present a number of challenges, many of which the authors' multiscale estimation framework is capable of addressing. They demonstrate the application of their estimation framework to an oceanographic data assimilation problem of considerable interest: the smoothing of Topex/Poseidon ocean altimetric data. they produce surface estimates and error statistics on a 512/spl times/512 grid in one minute on a Sparc-10. Using a generic surface reconstruction problem they demonstrate alternative uses of the multiscale framework: higher order state models, combining estimates from an ensemble of trees, and overlapping tree models.<<ETX>>
Convergence study of an iterative joint detector for wavelet packet multiple-access communication

The joint detection of all users in a multiple access communication system is known to greatly enhance the system performance. The optimal joint detector has a complexity which is exponential in the number of users and is, therefore, impractical. Suboptimal algorithms attempt to achieve near optimal performance with reduced computational complexity. The suboptimal joint detection literature deals only with receiver design, allowing for the use of any set of signature waveforms. Using the structure of the wavelet packet transform we are able to choose signal sets and design a detection algorithm which results in significantly lower computational complexity than other proposed suboptimal joint detectors. The wedding of detector development and wavelet packet signal set design, therefore, becomes an interesting problem. The convergence, performance and receiver complexity of suboptimal MA detectors crucially depend upon the signal set structure; it is, thus, of paramount importance to study the convergence of our iterative algorithm for our signal structure. Sufficient conditions for the convergence of an iterative joint detection scheme are developed.<<ETX>>
Multiscale representations of Markov random fields

Recently, a framework for multiscale stochastic modeling was introduced based on coarse-to-fine scale-recursive dynamics defined on trees. This model class has some attractive characteristics which lead to extremely efficient, statistically optimal signal and image processing algorithms. The authors show that this model class is also quite rich. In particular, they describe how 1-D Markov processes and 2-D Markov random fields (MRFs) can be represented within this framework. The recursive structure of 1-D Markov processes makes them simple to analyze, and generally leads to computationally efficient algorithms for statistical inference. On the other hand, 2-D MRFs are well known to be very difficult to analyze due to their noncausal structure, and thus their use typically leads to computationally intensive algorithms for smoothing and parameter identification. In contrast, their multiscale representations are based on scale-recursive models and thus lead naturally to scale-recursive algorithms, which can be substantially more efficient computationally than those associated with MRF models. In 1-D, the multiscale representation is a generalization of the midpoint deflection construction of Brownian motion. The representation of 2-D MRFs is based on a further generalization to a "midline" deflection construction. The exact representations of 2-D MRFs are used to motivate a class of multiscale approximate MRF models based on one-dimensional wavelet transforms. They demonstrate the use of these latter models in the context of texture representation and, in particular, they show how they can be used as approximations for or alternatives to well-known MRF texture models.
Toward nanometer-scale resolution in fluorescence microscopy using spectral self-interference

We introduce a new fluorescence microscopy technique that maps the axial position of a fluorophore with subnanometer precision. The interference of the emission of fluorophores in proximity to a reflecting surface results in fringes in the fluorescence spectrum that provide a unique signature of the axial position of the fluorophore. The nanometer sensitivity is demonstrated by measuring the height of a fluorescein monolayer covering a 12-nm step etched in silicon dioxide. In addition, the separation between fluorophores attached to the top or the bottom layer in a lipid bilayer film is determined. We further discuss extension of this microscopy technique to provide resolution of multiple layers spaced as closely as 10 nm for sparse systems.