nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive restoration of speckled SAR images using a compound random Markov field

This paper proposes a restoration scheme for noisy images generated by coherent imaging systems (e.g., synthetic aperture radar, synthetic aperture sonar ultrasound imaging, and laser imaging). The approach is Bayesian: the observed image intensity is assumed to be a random variable with gamma density; the image to be restored (mean amplitude) is modeled by a compound Gauss-Markov random field which enforces smoothness on homogeneous regions while preserving discontinuities between neighboring regions. A Neyman-Pearson detection criterion is used to infer the discontinuities, thus allowing to select a given false alarm probability maximizing the detection probability. The whole restoration scheme is then cast into a maximum a posteriori probability (MAP) problem. An expectation maximization type iterative scheme embedded in a continuation algorithm is used to compute the MAP solution. An application example performed on radar data is presented.
Minimum total variation in 3D ultrasound reconstruction

The paper proposes a Bayesian 3D ultrasound reconstruction/estimation from non-uniform ultrasound observations, non-Gaussian data fidelity term, and total variation (TV) based prior. To compute the maximum a posteriori (MAP) solution, we introduce a generalized expectation maximization (GEM) algorithm, which converges to the exact MAP solution in the case of convex data fidelity term. A set of experiments illustrates the effectiveness of the method.
Hyperspectral Subspace Identification

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.
Semi-supervised hyperspectral image classification based on a Markov random field and sparse multinomial logistic regression

This paper introduces a new semi-supervised classification and segmentation approach tailored to hyperspectral images. The posterior distributions of the classes are modeled by the multinomial logistic regression. The contextual information inherent to the spatial configuration of the image pixels is modeled by a Multi-Level Logistic (MLL) Markov-Gibbs random field. The multinomial logistic regressors, assumed to be random vectors with independent Laplacian components, are learned using the recently introduced LOR-SAL algorithm. The maximum a posteriori (MAP) segmentation is computed via the ¿-Expansion algorithm, a powerful graph cut based approach to integer optimization. The effectiveness of the proposed methodology is illustrated by classifying simulated and real data sets. Comparisons with state-of-art methods are also included.
Sparse Unmixing of Hyperspectral Data

Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer. In many situations, the identification of the end-member signatures in the original data set may be challenging due to insufficient spatial resolution, mixtures happening at different scales, and unavailability of completely pure spectral signatures in the scene. However, the unmixing problem can also be approached in semisupervised fashion, i.e., by assuming that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In practice, this is a combinatorial problem which calls for efficient linear sparse regression (SR) techniques based on sparsity-inducing regularizers, since the number of endmembers participating in a mixed pixel is usually very small compared with the (ever-growing) dimensionality (and availability) of spectral libraries. Linear SR is an area of very active research, with strong links to compressed sensing, basis pursuit (BP), BP denoising, and matching pursuit. In this paper, we study the linear spectral unmixing problem under the light of recent theoretical results published in those referred to areas. Furthermore, we provide a comparison of several available and new linear SR algorithms, with the ultimate goal of analyzing their potential in solving the spectral unmixing problem by resorting to available spectral libraries. Our experimental results, conducted using both simulated and real hyperspectral data sets collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infrared Imaging Spectrometer and spectral libraries publicly available from the U.S. Geological Survey, indicate the potential of SR techniques in the task of accurately characterizing the mixed pixels using the library spectra. This opens new perspectives for spectral unmixing, since the abundance estimation process no longer depends on the availability of pure spectral signatures in the input data nor on the capacity of a certain endmember extraction algorithm to identify such pure signatures.
Evaluation of bayesian hyperspectral image segmentation with a discriminative class learning

A Bayesian segmentation approach for hyperspectral images is introduced in this paper. The method improves the classification performance of discriminative classifiers by adding contextual information in the form of spatial dependencies. The technique herein presented builds the class densities based on fast sparse multinomial logistic regression and enforces spacial continuity by adopting a multi-level logistic Markov-Gibs prior. State-of-art performance of the proposed approach is illustrated in a set of experimental comparisons with recently introduced hyperspectral classification/segmentation methods.
Photon acceleration as the laser wakefield diagnostic for future plasma accelerators

In the near future, laser-plasma particle accelerators will be able to sustain electric fields in excess of 100 GeV/m, along plasma channels several Rayleigh lengths long. For these extreme conditions, present day laser wakefield diagnostics such as Frequency Domain Interferometry will not be able to resolve the wakefield structure and determine the magnitude of the electric field. In this paper, we present a detailed comparison between frequency-domain interferometry and a photon acceleration based wakefield diagnostic. We determine the experimental parameters for which photon acceleration becomes the only viable diagnostic technique. Dispersion effects on the probe beam and the implications of an arbitrary phase velocity of the plasma wave are discussed for both diagnostic techniques. We also propose an experimental set-up for a photon acceleration diagnostic allowing for the simultaneous measurement of the electric field structure and the laser wakefield phase velocity. Comparison with results from photon acceleration experiments by ionization fronts are also presented.
Wall Position And Thickness Estimation From Two-dimensional Echocardiograms

AbrtmctThis paper presents a model based method for endocardial (inner) and epicardial (outer) contour estimation in two-dimensional echocardiographic images. The setup herein introduced is appropriate to differeut echocardiographic views. In this work, however, it was fine-tuned for parasternal short axis view at the papillary muscle level. We use the Bayesian formulation and the maximum a priori (MAP) criterion. Contours are assumed one-dimensional noncausal Gauss-Markov random fields. The observed image pixels are modelled as Rayleigh distributed random variables with means depending on their positions (inside endocardium, between endocardium and pericardium, or outside pericardium). The method is implemented on an ordiuary workstation, and takes less than 10 seconds to estimate the inner and outer borders. Results with simulated and real images are presented.
Hyperspectral band selection using a collaborative sparse model

In our previous research, we have proposed band-similarity-based unsupervised band selection approaches, which are proven to be very efficient. In this paper, we propose to use a collaborative sparse model for further improvement. Specifically, the pre-selected bands using the fast method, called NFINDR+LP, are further refined using a collaborative sparse model. It not only requires that the linear regression coefficients are sparse, but also requires that the same set of active bands is shared by all the bands to be removed. With the collaborative sparseness constraint being relaxed, the final selected bands can be further improved, that is, the band subset with the same number of bands can provide better classification accuracy. Based on the preliminary result, the proposed sparse model is also capable of finding the minimum number of bands to be selected.
Group lapped iterative technique for fast solution of large linear systems

This work presents an iterative method for solving systems of linear equations. The technique is very much in the spirit of group iterative methods, allowing, however, lapped groups. For a wide class of matrices, the group lapped (GL) technique, herein introduced, improves the convergence rate by a large factor, demanding approximately the same number of operations per iteration as group iterative methods. For systems in which any subsystem is nonsingular and whose inverse matrix elements are null above (below) some upper (lower) diagonal, the exact solution is reached in just one step. Features of the GL method are illustrated with a numerical example.
Nonparametric estimation of mean velocity and spectral width in weather radar

Proposes a new nonparametric approach to the estimation of the mean Doppler velocity (first spectral moment) and the spectral width (square root of the second spectral centered moment) of a zero-mean stationary complex Gaussian process immersed in independent additive white Gaussian noise. By assuming that the power spectral density of the underlying process is bandlimited, the exact maximum likelihood estimates of its spectral moments are derived. An estimate based on the sample covariances is also studied. Both methods are robust in the sense that they do not rely on any assumption concerning the power spectral density (besides being bandlimited). Under weak conditions, the estimates based on sample covariances are best asymptotically normal.
Robust hyperspectral unmixing accounting for residual components

This paper presents a new hyperspectral mixture model jointly with a Bayesian algorithm for supervised hyperspectral unmixing. Based on the residual component analysis model, the proposed formulation assumes the linear model to be corrupted by an additive term that accounts for mismodelling effects (ME). The ME formulation takes into account the effect of outliers, the propagated errors in the signal processing chain and copes with some types of endmember variability (EV) or nonlinearity (NL). The known constraints on the model parameters are modeled via suitable priors. The resulting posterior distribution is optimized using a coordinate descent algorithm which allows us to compute the maximum a posteriori estimator of the unknown model parameters. The proposed model and estimation algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inferences and the computational complexity when compared to the state-of-the-art algorithms.
Phase unwrapping via diversity and graph cuts

Many imaging techniques, e.g., interferometric synthetic aperture radar, magnetic resonance imaging, diffraction tomography, yield interferometric phase images. For these applications, the measurements are modulo-2p, where p is the period, a certain real number, whereas the aimed information is contained in the true phase value. The process of inferring the phase from its wrapped modulo-2p values is the so-called phase unwrapping (PU) problem. In this paper we present a graph-cuts based PU technique that uses two wrapped images, of the same scene, generated with different periods p1, p2. This diversity allows to reduce the ambiguity effect of the wrapping modulo-2p operation, and is extensible to more than two periods. To infer the original data, we assume a first order Markov random field (MRF) prior and a maximum a posteriori probability (MAP) optimization viewpoint. The employed objective functionals have nonconvex, sinusoidal, data fidelity terms and a non isotropic total variation (TV) prior. This is an integer, nonconvex optimization problem for which we apply a technique that yields an exact, low order polynomial complexity, global solution. At its core is a non iterative graph cuts based optimization algorithm. As far as we know, all the few existing period diversity capable PU techniques for images, are either far too simplistic or employ simulated annealing, thus exponential complexity in time, optimization algorithms.
Robust collaborative nonnegative matrix factorization for hyperspectra unmixing (R-CONMF)

The recently introduced collaborative nonnegative matrix factorization (CoNMF) algorithm was conceived to simultaneously estimate the number of endmembers, the mixing matrix, and the fractional abundances from hyperspectral linear mixtures. This paper introduces R-CoNMF, which is a robust version of CoNMF. The robustness has been added by a) including a volume regularizer which penalizes the distance to a mixing matrix inferred by a pure pixel algorithm; and by b) introducing a new proximal alternating optimization (PAO) algorithm for which convergence to a critical point is guaranteed. Our experimental results indicate that R-CoNMF provides effective estimates both when the number of endmembers are unknown and when they are known.
Deconvolution of Poissonian images using variable splitting and augmented Lagrangian optimization

Although much research has been devoted to the problem of restoring Poissonian images, namely for medical and astronomical applications, applying the state of the art regularizers (such as those based on wavelets or total variation) to this class of images is still an open research front. This paper proposes a new approach to deconvolving Poissonian images, with the following building blocks: (a) the standard regularization/ maximum a posteriori (MAP) criterion, combining the Poisson log-likelihood with a regularizer (log-prior) is adopted; (b) the resulting optimization problem (which is hard, because the log-likelihood is non-quadratic and nonseparable and the regularizer is non-smooth) is transformed into an equivalent constrained problem, by a variable splitting procedure; (c) an augmented Lagrangian method is used to address this constrained problem. The resulting algorithm is shown to outperform alternative state-of-the-art methods.
Velocity estimation of fast moving targets using a single SAR sensor

A new methodology is presented to retrieve slant-range velocity estimates of moving targets inducing Doppler-shifts beyond the Nyquist limit determined by the pulse repetition frequency (PRF). The proposed approach exploits the linear dependence (not subject to PRF limitations) of the Doppler-shift with respect to the slant-range velocity, at each wavelength. Basically, we propose an algorithm to compute the skew of the two-dimensional spectral signature of a moving target. Distinctive features of this algorithm are its ability to cope with strong range migration and its efficiency from the computational point of view. By combining the developed scheme to retrieve the slant-range velocity with a methodology proposed earlier to estimate the velocity vector magnitude, the full velocity vector is unambiguously retrieved without increasing the mission PRF. The method gives effective results even when the returned echoes of the moving targets and the static ground overlap completely, provided that the moving targets signatures are digitally spotlighted and the signal-to-clutter ratio (SCR) is, roughly, greater than 14 dB. The effectiveness of the method is illustrated with simulated and real data. As an example, slant-range velocities of moving objects with velocities between 6 and 12 times the Nyquist velocity are estimated with accuracy better than 3%.
A new classification-oriented endmember extraction and sparse unmixing approach for hyperspectral data

Abundance information has been recently used to assist hyperspectral image classification by combining the information coming from classification and unmixing. The fact that classes are usually inconsistent with endmembers makes it a crucial issue to find possible connections between classification and unmixing. This paper describes a new class-based endmember extraction and sparse unmixing approach aimed at establishing the correspondence between endmembers and classes. The proposed approach is exploited in a semisupervised classification framework that combines classification and unmixing with active learning (AL). During the AL process, the class probabilities and abundance information are exploited simultaneously to select the most informative unlabeled samples for classification purposes. Our approach adopts a well-established discriminative probabilistic classifier, the multinomial logistic regression (MLR), to learn the class posterior probabilities. The effectiveness of the proposed method is evaluated using real hyperspectral data set collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infrared Imaging Spectrometer (AVIRIS) over the Indian Pines region, Indiana.
On the use of collaborative sparse regression in hyperspectral unmixing chains

Hyperspectral unmixing is a complex process in which several steps are consecutively executed to derive the desired results: the image endmembers and their corresponding fractional abundance maps. Each of these unmixing stages benefits nowadays from a plethora of algorithms, continuously developed and improved. In this paper, we analyze three of the general unmixing steps: band selection (data dimensionality reduction), endmember extraction and fractional abundance inference (inversion) from a multi-measurement vector problem point of view. We show that these particular steps can be expressed as a convex optimization problem in which the concept of data collaborativity is exploited and one single algorithm can be efficiently used to solve them. Our experimental results obtained in an urban dataset acquired over Berlin, Germany, show the potential of this approach in remote sensing applications.
Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields

This paper introduces a new supervised segmentation algorithm for remotely sensed hyperspectral image data which integrates the spectral and spatial information in a Bayesian framework. A multinomial logistic regression (MLR) algorithm is first used to learn the posterior probability distributions from the spectral information, using a subspace projection method to better characterize noise and highly mixed pixels. Then, contextual information is included using a multilevel logistic Markov-Gibbs Markov random field prior. Finally, a maximum a posteriori segmentation is efficiently computed by the min-cut-based integer optimization algorithm. The proposed segmentation approach is experimentally evaluated using both simulated and real hyperspectral data sets, exhibiting state-of-the-art performance when compared with recently introduced hyperspectral image classification methods. The integration of subspace projection methods with the MLR algorithm, combined with the use of spatial-contextual information, represents an innovative contribution in the literature. This approach is shown to provide accurate characterization of hyperspectral imagery in both the spectral and the spatial domain.
Efficient detection and ground mapping of selected moving targets using SAR raw-data

This work addresses moving targets detection and imaging using airborne SAR data. Targets are selected according to user-defined radial velocity and direction. The computational burden is drastically reduced because azimuth compression is done only for ranges with a positive moving target indication. Furthermore, the knowledge of the velocity sign allows to tune the detection and focusing of moving targets with higher SNR than the usual methods. Range migration is easily tackled because radial velocity is known, enabling the correct compression filter design.
Fast frame-based image deconvolution using variable splitting and constrained optimization

We propose a new fast algorithm for solving one of the standard formulations of frame-based image deconvolution: an unconstrained optimization problem, involving an ℓ2 data-fidelity term and a non-smooth regularizer. Our approach is based on using variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. Experiments on a set of image deblurring benchmark problems show that our algorithm is clearly faster than previous state-of-the-art methods.
Hyperspectral image superresolution: An edge-preserving convex formulation

Hyperspectral remote sensing images (HSIs) are characterized by having a low spatial resolution and a high spectral resolution, whereas multispectral images (MSIs) are characterized by low spectral and high spatial resolutions. These complementary characteristics have stimulated active research in the inference of images with high spatial and spectral resolutions from HSI-MSI pairs. In this paper, we formulate this data fusion problem as the minimization of a convex objective function containing two data-fitting terms and an edge-preserving regularizer. The data-fitting terms are quadratic and account for blur, different spatial resolutions, and additive noise; the regularizer, a form of vector Total Variation, promotes aligned discontinuities across the reconstructed hyperspectral bands. The optimization described above is rather hard, owing to its non-diagonalizable linear operators, to the non-quadratic and non-smooth nature of the regularizer, and to the very large size of the image to be inferred. We tackle these difficulties by tailoring the Split Augmented Lagrangian Shrinkage Algorithm (SALSA) - an instance of the Alternating Direction Method of Multipliers (ADMM) - to this optimization problem. By using a convenient variable splitting and by exploiting the fact that HSIs generally “live” in a low-dimensional subspace, we obtain an effective algorithm that yields state-of-the-art results, as illustrated by experiments.