Spatial Continuity Constraint Research Articles

A novel high-resolution imaging method based on sparsity in the time domain and spectral fitting in the frequency domain

During the propagation of seismic waves underground, the high-frequency seismic response of thin reservoir is absorbed and attenuated, which poses a challenge in seismic thin reservoir prediction. The high-resolution processing techniques have the capability to significantly expand the frequency range of the seismic data, so it becomes a key technique for thin reservoir prediction. Most of these techniques necessitate the extraction of seismic wavelets. However, the spatial and temporal variations of seismic data result in multiple solutions for wavelet extraction. Simultaneously, the majority of techniques fail to consider the influence of spatial tectonic features on the high-resolution processing. In this paper, we propose a novel solution to address these two fundamental challenges by utilizing seismic spectral expansion, sparse reflection coefficients, and spatial continuity constraints. First, we propose an innovative spectral fitting method that aims to expand the frequency bandwidth while adhering to the desired wavelet constraints. This method allows us to fully utilize the effective frequency information. It not only obtains broadband seismic data but also captures precise wavelets. Then, sparse deconvolution is employed to further extend the frequency range by utilizing the accurately expected wavelet and obtaining a high-resolution reflection coefficient. Finally, the Hessian matrix regularization is employed to constrain the spatial continuity of the reflection coefficient. This method is validated in both the model and real seismic data. Compared to traditional sparse deconvolution and spectral modeling deconvolution with spatial constraints, this method not only expands the frequency bandwidth and enhances seismic resolution but also preserves operational frequency information and improves the spatial continuity of seismic data. It has been verified that this approach can be used to forecast thin reservoir and reconstruct spatial tectonic characteristics.

A Segmentation-Based CFAR Detector With Spatial Continuity Constraint in Nonhomogeneous Weather Clutter

A Segmentation-Based CFAR Detector With Spatial Continuity Constraint in Nonhomogeneous Weather Clutter

Spatiotemporal imagery selection for full coverage image generation over a large area with HFA-Net based quality grading

ABSTRACT Remote sensing images often need to be merged into a larger mosaic image to support analysis on large areas in many applications. However, the performance of the mosaic imagery may be severely restricted if there are many areas with cloud coverage or if these images used for merging have a long-time span. Therefore, this paper proposes a method of image selection for full coverage image (i.e. a mosaic image with no cloud-contaminated pixels) generation. Specifically, a novel High-Frequency-Aware (HFA)-Net based on Swin-Transformer for region quality grading is presented to provide a data basis for image selection. Spatiotemporal constraints are presented to optimize the image selection. In the temporal dimension, the shortest-time-span constraint shortens the time span of the selected images, obviously improving the timeliness of the image selection results (i.e. with a shorter time span). In the spatial dimension, a spatial continuity constraint is proposed to select data with better quality and larger area, thus improving the radiometric continuity of the results. Experiments on the GF-1 images indicate that the proposed method reduces the averages by 76.1% and 38.7% in terms of the shortest time span compared to the Improved Coverage-oriented Retrieval algorithm (MICR) and Retrieval Method based on Grid Compensation (RMGC) methods, respectively. Moreover, the proposed method also reduces the residual cloud amount by an average of 91.2%, 89.8%, and 83.4% when compared to the MICR, RMGC, and Pixel-based Time-series Synthesis Method (PTSM) methods, respectively.

Second-Order Ultrasound Elastography With L1-Norm Spatial Regularization.

Time delay estimation (TDE) between two radio-frequency (RF) frames is one of the major steps of quasi-static ultrasound elastography, which detects tissue pathology by estimating its mechanical properties. Regularized optimization-based techniques, a prominent class of TDE algorithms, optimize a nonlinear energy functional consisting of data constancy and spatial continuity constraints to obtain the displacement and strain maps between the time-series frames under consideration. The existing optimization-based TDE methods often consider the L2 -norm of displacement derivatives to construct the regularizer. However, such a formulation over-penalizes the displacement irregularity and poses two major issues to the estimated strain field. First, the boundaries between different tissues are blurred. Second, the visual contrast between the target and the background is suboptimal. To resolve these issues, herein, we propose a novel TDE algorithm where instead of L2 -, L1 -norms of both first- and second-order displacement derivatives are taken into account to devise the continuity functional. We handle the non-differentiability of L1 -norm by smoothing the absolute value function's sharp corner and optimize the resulting cost function in an iterative manner. We call our technique Second-Order Ultrasound eLastography (SOUL) with the L1 -norm spatial regularization ( L1 -SOUL). In terms of both sharpness and visual contrast, L1 -SOUL substantially outperforms GLobal Ultrasound Elastography (GLUE), tOtal Variation rEgulaRization and WINDow-based time delay estimation (OVERWIND), and SOUL, three recently published TDE algorithms in all validation experiments performed in this study. In cases of simulated, phantom, and in vivo datasets, respectively, L1 -SOUL achieves 67.8%, 46.81%, and 117.35% improvements of contrast-to-noise ratio (CNR) over SOUL. The L1 -SOUL code can be downloaded from http://code.sonography.ai.

Fragmentation metrics and fragmentation-aware algorithm for spectrally/spatially flexible optical networks

Spectrally/spatially flexible optical networks (SS-FONs) are a promising solution to future traffic requirements in optical backbone networks. SS-FONs exploit the spatial dimension to increase the network capacity, while preserving the resource management flexibility, as they still operate within a flex-grid composed of small frequency slots (slices). A flex-grid makes it possible to realize transmission using super-channels (SChs) that comprise a set of contiguous slots. In this paper, we focus on spectral SChs, i.e., a SCh realized only on one spatial mode on each fiber. In SS-FONs, setting up and tearing down multiple lightpath requests within a flex-grid may result in spectrum fragmentation and, in turn, blocking of requests. In this work, we investigate several fragmentation metrics in a SS-FON. The problem of identifying appropriate metrics to measure fragmentation has been investigated in single-core elastic optical networks, but to the best of our knowledge, there is no such research available for SS-FONs. Therefore, we propose several fragmentation metrics for SS-FONs. We introduce the concept of bordering super-channels (B-SChs), i.e., SChs whose spectrum is allocated at the border of already allocated spectrum slots, which show promising results with regard to minimizing fragmentation. The investigation of all candidate B-SChs allows us to find the one that minimizes the network fragmentation and, in turn, spectrum waste. Hence, we propose a fragmentation-aware (FA) algorithm with bordering SChs that assigns optical resources to dynamic requests by utilizing information from the proposed fragmentation metrics and the set of candidate B-SChs. Experiments on a representative network topology show that the investigation of multiple B-SChs in the FA algorithm reduces the blocking probability when compared to the reference FA algorithms. Finally, we analyze the impact of a spatial continuity constraint on the network fragmentation.

A user-driven feedback approach for 2D direct current resistivity inversion based on particle swarm optimization

We have developed an inversion approach based on particle swarm optimization (PSO) for 2D direct current resistivity data. Once the PSO algorithm is implemented, cost-function changes on constraints and misfit criteria can be easily performed and an approximate invariance of the Lagrange multipliers is obtained if each term of the cost function is properly normalized. More importantly, the interpreter can divide the mesh into partitions and apply a different constraint or combination of constraints in each partition. We explore several restrictions on the log-resistivity variation, either in single or in joint versions, including spatial continuity constraints in the [Formula: see text]-and [Formula: see text]-norms, total variation, sparsity constraints using the discrete cosine transform and Daubechies bases, and minimum moment of inertia constraints to impose concentration of resistive or conductive materials along target axes. In the latter case, the earth surface might be used also as a target axis. In addition, the final state of the PSO algorithm, once a stopping condition has been reached, contains not only the best solution but also a cluster of good suboptimal solutions that can be used for uncertainty analysis. As a result, the interpreter has the flexibility to perform an interpretation process based on a feedback trial-and-error inversion approach, in a similar manner that he/she has when using a friendly forward modeling software. The interpreter can then drive the tentative solutions obtained along the inversion process to incorporate their conceptions about the geologic environment, besides appraising misfit and stability of the solutions. We evaluate synthetic and field data examples. In the first case, we determine how the interpreter can drive the inversion process in a karst environment to image dissolution features. In the second case, we explore a similar situation aiming to optimize borehole locations in fracture zones in crystalline rocks.

Exploring matrix and tensor factorization for discovering latent structures in large echosounder datasets

Moored autonomous echosounders are increasingly popular as an integrated component in ocean observing systems for measuring biological response to environmental changes. Different from ship-based observations, these large echo datasets often lack concurrent ground truth information from net or optical samples and reliable calibration, both of which are required for conventional analysis routines. However, even though accurate inference of organism abundance is not possible in such scenarios, rich spatio-temporal structures exist in the data and may be exploited to capture variation of migration patterns of different animal groups in the water column. Here, we explore the use of unsupervised matrix and tensor factorization approaches to analyze the long-term echo data sets from the Ocean Observatories Initiative (OOI) network. The data stretch in multiple dimensions, including time, space, and frequency. We restructure the echo data to explore latent structures at different temporal scales using different factorization formulations. Outputs from different methods are compared among one another and with those from conventional echo analysis routines. The results show the importance of augmenting generic factorization formulations with temporal and spatial continuity constraints for biologically meaningful analysis of large echosounder datasets.

Computationally Efficient Algorithms for Sparse, Dynamic Solutions to the EEG Source Localization Problem.

Electroencephalography (EEG) and magnetoencephalography noninvasively record scalp electromagnetic fields generated by cerebral currents, revealing millisecond-level brain dynamics useful for neuroscience and clinical applications. Estimating the currents that generate these fields, i.e., source localization, is an ill-conditioned inverse problem. Solutions to this problem have focused on spatial continuity constraints, dynamic modeling, or sparsity constraints. The combination of these key ideas could offer significant performance improvements, but substantial computational costs pose a challenge for practical application of such approaches. Here, we propose a new method for EEG source localization that combines: 1) covariance estimation for both source and measurement noises; 2) linear state-space dynamics; and 3) sparsity constraints, using 4) novel computationally efficient estimation algorithms. For source covariance estimation, we use a locally smooth basis alongside sparsity enforcing priors. For EEG measurement noise covariance estimation, we use an inverse Wishart prior density. We estimate these model parameters using an expectation-maximization algorithm that employs steady-state filtering and smoothing to expedite computations. We characterized the performance of our method by analyzing simulated data and experimental recordings of eyes-closed alpha oscillations. Our sparsity enforcing priors significantly improved estimation of both the spatial distribution and time course of simulated data, while improving computational time by more than 12-fold over previous dynamic methods. We developed and demonstrated a novel method for improved EEG source localization employing spatial covariance estimation, dynamics, and sparsity. Our approach provides substantial performance improvements over existing methods using computationally efficient algorithms that will facilitate practical applications in both neuroscience and medicine.

MDS-based segmentation model for the fusion of contour and texture cues in natural images

In this paper, we present an original image segmentation model based on a preliminary spatially adaptive non-linear data dimensionality reduction step integrating contour and texture cues. This new dimensionality reduction model aims at converting an input texture image into a noisy color image in order to greatly simplify its subsequent segmentation. In this latter de-texturing model, the (spatially adaptive) non-local constraints based on edge and contour cues allows us to efficiently regularize the reduced data (or the resulting de-textured color image) and to efficiently combine inhomogeneous region and edge based features in a data fusion/reduction model used as pre-processing step for a final segmentation task. In addition, a set of color/texture and edge-based adaptive spatial continuity constraints is imposed during the segmentation step. These improvements lead to an appealing and powerful two-step adaptive segmentation model, integrating contour and texture cues. Extensive experimental evaluation on the Berkeley image segmentation database demonstrates the efficiency of this hybrid segmentation model in terms of classification accuracy of pairwise pixels in the resulting segmentation map and in the precision–recall framework widespread used for evaluating contour detectors.

Packet Video Error Concealment With Auto Regressive Model

In this paper, auto regressive (AR) model is applied to error concealment for block-based packet video coding. In the proposed error concealment scheme, the motion vector for each corrupted block is first derived by any kind of recovery algorithms. Then each pixel within the corrupted block is replenished as the weighted summation of pixels within a square centered at the pixel indicated by the derived motion vector in a regression manner. Two block-dependent AR coefficient derivation algorithms under spatial and temporal continuity constraints are proposed respectively. The first one derives the AR coefficients via minimizing the summation of the weighted square errors within all the available neighboring blocks under the spatial continuity constraint. The confidence weight of each pixel sample within the available neighboring blocks is inversely proportional to the distance between the sample and the corrupted block. The second one derives the AR coefficients by minimizing the summation of the weighted square errors within an extended block in the previous frame along the motion trajectory under the temporal continuity constraint. The confidence weight of each extended sample is inversely proportional to the distance toward the corresponding motion aligned block whereas the confidence weight of each sample within the motion aligned block is set to be one. The regression results generated by the two algorithms are then merged to form the ultimate restorations. Various experimental results demonstrate that the proposed error concealment strategy is able to improve both the objective and subjective quality of the replenished blocks compared to other methods.

Identifying disease-associated SNP clusters via contiguous outlier detection

Although genome-wide association studies (GWAS) have identified many disease-susceptibility single-nucleotide polymorphisms (SNPs), these findings can only explain a small portion of genetic contributions to complex diseases, which is known as the missing heritability. A possible explanation is that genetic variants with small effects have not been detected. The chance is < 8 that a causal SNP will be directly genotyped. The effects of its neighboring SNPs may be too weak to be detected due to the effect decay caused by imperfect linkage disequilibrium. Moreover, it is still challenging to detect a causal SNP with a small effect even if it has been directly genotyped. In order to increase the statistical power when detecting disease-associated SNPs with relatively small effects, we propose a method using neighborhood information. Since the disease-associated SNPs account for only a small fraction of the entire SNP set, we formulate this problem as Contiguous Outlier DEtection (CODE), which is a discrete optimization problem. In our formulation, we cast the disease-associated SNPs as outliers and further impose a spatial continuity constraint for outlier detection. We show that this optimization can be solved exactly using graph cuts. We also employ the stability selection strategy to control the false positive results caused by imperfect parameter tuning. We demonstrate its advantage in simulations and real experiments. In particular, the newly identified SNP clusters are replicable in two independent datasets. The software is available at: http://bioinformatics.ust.hk/CODE.zip. eeyu@ust.hk Supplementary data are available at Bioinformatics online.