Post-processing Procedure Research Articles

An Improved SRGAN Based Ambiguity Suppression Algorithm for SAR Ship Target Contrast Enhancement

Due to the specific characteristics of synthetic aperture radar (SAR), there will be ambiguity interference in SAR images, resulting in low contrast of the ship target to the clutter. This letter proposes an improved super-resolution generative adversarial network (ISRGAN) based ambiguity suppression algorithm for SAR ship target contrast enhancement. The proposed ISRGAN is the first attempt of using GAN for SAR ambiguity suppression. As a post-processing procedure, it does not need prior information of SAR systems, so it can be applied to various observation scenes and different acquisition modes. The generator of ISRGAN embeds the residual dense network (RDN) to optimally fuse the global and local features of the image, and it effectively improves the completeness of the feature information used for SAR ship target contrast enhancement. The superiority of ISRGAN on ambiguity suppression is validated on the Chinese Gaofen-3 imagery.

Effective Clustering via Structured Graph Learning

Given an affinity graph of data samples, graph-based clustering aims to partition these samples into disjoint groups based on the affinities, and most previous works are based on spectral clustering. However, two problems among spectral-based methods heavily affect the clustering performance. Firstly, the randomness of post-processing procedures, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means, affects the stability of clustering. Secondly, the separated stages of spectral-based methods, including graph construction, spectral embedding learning, and clustering decision, lead to mismatched problems. In this paper, we explore a structured graph learning (SGL) framework that aims to fuse these stages to improve clustering stability. Specifically, SGL adaptively learns a structured affinity graph that contains exact <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> connected components. Each connected component corresponds to a cluster so clustering assignments can be directly obtained according to the connectivity of the learned graph. In this way, SGL avoids the randomness brought by reliance on traditional post-processing procedures. Meanwhile, the graph construction and structured graph learning procedures happen simultaneously, which alleviates the mismatched problem effectively. Moreover, we propose an efficient algorithm to solve the involved optimization problems and discuss the connections between this work and previous works. Numerical experiments on several synthetic and real datasets demonstrate the effectiveness of our methods.

Image Deformation Estimation via Multiobjective Optimization

The free-form deformation model can represent a wide range of non-rigid deformations by manipulating a control point lattice over the image. However, due to a large number of parameters, it is challenging to fit the free-form deformation model directly to the deformed image for deformation estimation because of the complexity of the fitness landscape. In this paper, we cast the registration task as a multi-objective optimization problem (MOP) according to the fact that regions affected by each control point overlap with each other. Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs). In addition, a coarse-to-fine strategy is realized by image pyramid combined with control point mesh subdivision. Specifically, the optimized candidate solutions of the current image level are inherited by the next level, which increases the ability to deal with large deformation. Also, a post-processing procedure is proposed to generate a single output utilizing the Pareto optimal solutions. Comparative experiments on both synthetic and real-world images show the effectiveness and usefulness of our deformation estimation method.

Deep Learning-Based Branch-Cut Method for InSAR Two-Dimensional Phase Unwrapping

Two-dimensional (2-D) phase unwrapping (PU) is a critical processing step for many synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the traditional 2-D PU is an ill-posed inverse problem, which means that regardless of how skillful the PU algorithm designer is, it is impossible to design an algorithm that can correctly process all the 2-D PU situations, i.e., we can only design the best PU algorithm in the statistical sense. Therefore, accumulating PU processing experience from different study cases is important for PU algorithm design. Currently, the deep learning (DL) technique provides a potential framework to accumulate processing experience, and a flood of valuable data coming from different InSAR sensors provides the ability to enable the learning-based PU technique outside the traditional model-based technique. In this article, we transform the 2-D PU problem into a learnable image semantic segmentation problem and propose a DL-based branch-cut deployment method (abbreviated as BCNet). To start, we propose the optimal branch-cut connection criterion (referred to as OPT-BC) with the reference unwrapped phase given. Next, using the relationship between the residue and branch-cut as the learning objective, BCNet is trained using the samples provided by OPT-BC to produce the branch-cut result. Finally, the traditional branch-cut method is utilized to perform the postprocessing procedure to obtain the final PU result. The experimental results demonstrate that the proposed BCNet-based PU method is a near-real-time 2-D PU algorithm, and its accuracy outperforms the traditional model- and learning-based 2-D PU methods.

Fatigue crack propagation studies based on the plastic component of the CTOD evaluated from Digital Image Correlation data

This work presents a new approach based on the Crack Tip Opening Displacement (CTOD) for studying the fatigue crack growth. The plastic component of the CTOD is employed for predicting the crack propagation on austenitic stainless steel. This is in contrast to Linear Elastic Fracture Mechanics analysis based on the Stress Intensity Factor (SIF) (Ewalds and Wanhill 1984). The current method is also valid beyond Small Scale Yielding (Antunes et al. 2016)(Antunes et al. 2017a)(Borges et al. 2020) as it incorporates naturally the plasticity effects taking place at the crack tip (Lopez-Crespo et al. 2018). The approach is based on post-processing the full-field displacement information generated by Digital Image Correlation technique (Chernyatin et al. 2018). Such technique is used to monitor the crack advance throughout the test (Cruces et al. 2020). The post-processing procedure employed for extracting the plastic component of the CTOD is described in detail. The results on steel CT specimens appear very promising.

Wave Surface Identification Based on Stereo Vision and Wave Theory: an Initial Attempt

Wave height is the most basic element of wave information, and accurate measurement of wave height plays a crucial role in both understanding wave theory and verifying numerical models. In this paper, an improved optical measurement system was proposed based on the principle of binocular stereo vision, which breaks through the limitations of traditional measurement equipment such as wave gauges and other single-point measurements, and improves the commonly used optical measurement method that relies solely on images by involving water wave theory into the data post-processing procedure. By identifying and reconstructing the unidirectional regular wave surface in a towing tank and comparing the results with the wave gauges and theoretical incoming wave parameters for validation, the results show that the measurement system has an error of about 1% in the measurement of a wide area. In the end of the paper, a preliminary attempt is made on the irregular wave surface identification.

Evaluation of intracellular distribution of folate functionalized silica nanoparticles using fluorescence and hyperspectral enhanced dark field microscopy.

Using nanoparticles as carriers for drug delivery systems has become a widely applied strategy in therapeutics and diagnostics. However, the pattern of their intracellular distribution is yet to be clarified. Here we present an in vitro study on the incorporation of mesoporous silica nanoparticles conjugated with folate and loaded with a cytotoxic drug, Irinotecan. The nanoparticles count and distribution within the cell frame were evaluated by means of enhanced dark field microscopy combined with hyperspectral imagery and 3D reconstructions from double-labeled fluorescent samples. An original post-processing procedure was developed to emphasize the nanoparticles' localization in 3D reconstruction of cellular compartments. By these means, it has been shown that the conjugation of mesoporous silica nanoparticles with folate increases the efficiency of nanoparticles entering the cell and their preferential localization in the close vicinity of the nucleus. As revealed by metabolic viability assays, the nanoparticles functionalized with folate enhance the cytotoxic efficiency of Irinotecan.

Nationwide Radar-Based Precipitation Nowcasting—A Localization Filtering Approach and its Application for Germany

One approach to improve advection methods is the short-term ensemble prediction system (STEPS). STEPS decomposes precipitation fields into different spatial scales and filters those having a short lifetime. The latter is achieved by using an auto-regressive (AR) model that considers a sequence of observations. However, such a model tends to smooth nowcasting fields especially in small but convective precipitation areas and at longer lead-times. With focus on the deterministic configuration of STEPS, i.e., the spectral prognosis model (SPROG), this article 1) extends the STEPS approach by estimating spatially localized parameters of the AR process, 2) conducts a sensitivity analysis of the SPROG model to the order of the AR process, the spatial decomposition levels, and post-processing, and 3) analyzes the forecast skill of the extended STEPS. For such purpose, the performance of the localized AR model was demonstrated and evaluated at several precipitation thresholds and window sizes using a varied set of precipitation events collected by the radar network of the German Weather Service. The statistical results exhibited an improved performance of the localized AR model over SPROG when both are evaluated at precipitation thresholds and window sizes larger than 0.1 mm h <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula> and 1 km, respectively, and for lead-times up to 2 h. The analysis suggested a first-order AR process, six cascade levels, and a mean adjustment post-processing procedure. Our results show a key role of the localization aspect when generating nationwide forecasts in scenarios that include large precipitation areas which are non-uniformly distributed having isolated convective features.

Accelerated reconstruction of dictionary-based T2 relaxation maps based on dictionary compression and gradient descent search algorithms

BackgroundQuantitative T2-relaxation-based contrast maps have shown to be highly beneficial for clinical diagnosis and follow-up. The generation of quantitative maps, however, is impaired by long acquisition times, and time-consuming post-processing schemes. The EMC platform is a dictionary-based technique, which involves simulating theoretical signal curves for different physical and experimental values, followed by matching the experimentally acquired signals to the set simulated ones.PurposeAlthough the EMC technique has shown to produce accurate T2 maps, it involves computationally intensive post-processing procedures. In this work we present an approach for accelerating the reconstruction of T2 relaxation maps.MethodsThis work presents two alternative post-processing approaches for accelerating the reconstruction of EMC-based T2 relaxation maps. These are (a) Dictionary compression using principal component analysis (PCA) and (b) gradient-descent search algorithm. Additional acceleration was achieved by finding the optimal MATLAB C++ compiler. The utility of the two suggested approaches was examined by calculating the relative error, produced by each technique.ResultsGradient descent method was in perfect agreement with the ground truth exhaustive search matching process. PCA based acceleration produced root mean square error (RMSE) of up to 4% compared to exhaustive matching process. Overall acceleration of x16 was achieved using gradient descent in addition to x7 acceleration by choosing the optimal MATLAB C++ compiler.ConclusionsPostprocessing of EMC-based T2 relaxation maps can be accelerated without impairing the accuracy of the ensuing T2 values.

Novel 2D strain-rate-dependent lamina-based and RVE/phase-based progressive fatigue damage criteria for randomly loaded multi-layer fiber-reinforced composites

Two implicit progressive fatigue damage models that rely on new equivalent-damage and equivalent-stress criteria are presented for the prediction of various failure modes of the composites. The criteria are coupled with lamina-based and representative-volume-element-based damage progression approaches. The common concepts of residual strength and residual stiffness are revisited and modified. A fatigue life assessment algorithm that incorporates the strain-rate-dependence of the fatigue strengths and stiffnesses, and random and asynchronous changes of the stress components, distinct mean values, and phase shifts of the stress components is employed. New ideas and new post-processing procedures are employed in the current research. It is the first time that the significant impacts of the strain-rate-dependence of the properties of the composites on stress and fatigue life analyses are investigated. Results of the proposed fatigue criteria are first implemented to a composite plate with a complex lamination scheme under a random transverse load and the predicted fatigue lives are verified by the experimental results. Then, these criteria are implemented to a composite chassis frame of an SUV car under realistic random road inputs and the theoretical results are verified by the experimental results. Results confirm the significant role of the strain-rate-dependence effects on the fatigue lives.

Hybridization of the virtual element method for linear elasticity problems

In this paper, we extend the hybridization procedure proposed in [D. N. Arnold and F. Brezzi, Mixed and nonconforming finite element methods: Implementation, postprocessing and error estimates, ESAIM Math. Model. Numer. Anal. 19 (1985) 7–32] to the Virtual Element Method for linear elasticity problems based on the Hellinger–Reissner principle. To illustrate such a technique, we focus on a specific 2D scheme, but other methods and 3D problems can be considered as well. We also show how to design a better approximation of the displacement field using a straightforward post-processing procedure. The numerical experiments confirm the theory for both two and three-dimensional problems.

Robust doubly stochastic graph clustering

Graph clustering has achieved promising performance in various real-world applications, and attracted sufficient attention in machine learning. Generally, it encodes the samples’ relationship with an affinity graph, and then conducts graph-theoretic optimization to partition the samples into clusters. The performance of the graph clustering methods may be affected by many factors, i.e., the graph quality, the loss measurement and the ad hoc post-processing. In this paper, a new Robust Doubly Stochastic graph clustering method (RDS) is presented, which has the following advantages: (1) it learns a doubly stochastic graph with the self-expression strategy automatically, and does not need the graph normalization step to improve the graph quality; (2) it utilizes a new loss function to calculate the approximation error, which is robust to the outliers that far from the normal samples; (3) it generates the cluster indicator according to the learned graph directly, such that the uncertainty caused by the post-processing procedure can be avoided. Extensive experiments on eight benchmarks demonstrate the effectiveness of RDS on data clustering, and show its advantages over the previous graph clustering methods are also verified.

Surface reconstruction post-processing method for 3D- scanned objects

3D scanning is widely used in multiple applications to obtain high precision / non-destructive documentation of real-life objects, which is especially important in Cultural Heritage (CH) preservation. However, some issues (in particular missing parts which are commonly known as “holes”) affect the accuracy of the obtained 3D model after the scanning procedure and requires time-consuming post-processing procedures, which include manual editing by highly-trained personnel. In this article an automatic method to reconstruct the obtained surface of 3D models is proposed, improving previously obtained results for high-density point clouds.

PU-M-Net for phase unwrapping with speckle reduction and structure protection in ESPI

In this paper, we propose a deep nonlinear CNN model, named as PU-M-Net for phase unwrapping with speckle reduction and structure protection in ESPI. Our PU-M-Net consists of four pathways in “M” shape, and merges them by abundant skip connections. By this means, our PU-M-Net can improve the flow of deep features, promote the fusion of shallow information and deep features, and enhance the utilization of initial features in phase unwrapping. We propose a structure-consideration loss function by combining the MAE and MS-SSIM loss functions, and also construct an available pertinent dataset for ESPI phase unwrapping by means of the phase shifting method. With the proposed dataset and the proposed loss function, we train the PU-M-Net successfully under a limited training condition. With the trained network, we directly get the results from the original wrapped phases. We test our method on many simulated and experimental ESPI wrapped phases, and compare it with a least-squares method, a quality-guided method, and two deep learning methods. We also compared it with the PU-M-Net trained by MSE loss function. The performance is evaluated quantitatively and qualitatively in terms of phase unwrapping and structure protection. Results demonstrate that our method can reduce speckles and protect structures in phase unwrapping, and get the better results than the compared methods. Besides, it does not require any parameter fine-turning and any pre-process or post-process procedure, even for the case of high-level speckles or high-dense wrapped phases. Moreover, it has full advantages on excellent generalization and batch performance, and can be used to process a great number of images rapidly. Indeed, it has been applied to the dynamic measurement successfully in this paper.

A configurational force-based material point method for crack propagation modelling in 2D

The modelling of fracture initiation and propagation is a nontrivial problem in computational mechanics. However, it is an area that is extremely important in engineering applications, requiring accurate and robust numerical methods that can be applied to a variety of materials. This paper presents the development of a new numerical modelling approach, which combines material, or configurational, forces and the material point method (MPM), for finite deformation crack modelling of linear elastic solids in two dimensions. The combination of these numerical methods offers a number of advantages relating to the flexibility of the MPM in terms of decoupling the material deformation from the computational grid and the general nature of configurational force theory in terms of being applicable across different material behaviour. In the method presented in this paper, the MPM forms the basis of the mechanical response of the underlying material, while the configurational force theory provides a fracture criterion for crack modelling through a post-processing procedure. The developed modelling framework is applied to a number of benchmark problems for linear elastic solids in 2D. All simulations show good agreement with the results in the literature, which demonstrates that the combined configuration force-material point framework is a promising numerical tool for fracture modelling.

Optimization-Based Network Identification for Thermal Transient Measurements

Network identification by deconvolution is a proven method for determining the thermal structure function of a given device. The method allows to derive the thermal capacitances as well as the resistances of a one-dimensional thermal path from the thermal step response of the device. However, the results of this method are significantly affected by noise in the measured data, which is unavoidable to a certain extent. In this paper, a post-processing procedure for network identification from thermal transient measurements is presented. This so-called optimization-based network identification provides a much more accurate and robust result compared to approaches using Fourier or Bayesian deconvolution in combination with Foster-to-Cauer transformation. The thermal structure function obtained from network identification by deconvolution is improved by repeatedly solving the inverse problem in a multi-dimensional optimization process. The result is a non-diverging thermal structure function, which agrees well with the measured thermal impedance. In addition, the associated time constant spectrum can be calculated very accurately. This work shows the potential of inverse optimization approaches for network identification.

A novel parameter derived from post-processing procedure of dual energy CT for identification of gout

ROI analysis is frequently used for obtaining acid content on rapid-kV-switching dual energy CT (DECT), providing inadequate accuracy. A new parameter derived from post-processing procedure, maximum lower limit with stain visible (MLLSV), was used by us to diagnose gout. 30 gout patients and 20 healthy volunteers were analyzed by using MLLSV. MLLSV was defined as the maximum lower limit of display window allowing only one stained site visible. Radiologists were asked to continuously increase the lower limit of display window of uric acid to decrease number of stained sites until the last stained site disappeared. MLLSV obtained by this way was compared between gout patients and volunteers. Receiver operating characteristic (ROC) curve was used to determine the performance. MLLSV of gout patients was significantly higher than that of volunteers (1373.3 ± 23.0 mg/cm3 vs. 1315.4 ± 20.7 mg/cm3, p = 0.000). The area under ROC curve of MLLSV was 0.993 in identifying gout. When using the optimal cutoff of 1342 mg/cm3, the sensitivity and specificity of MLLSV in identification of gout were 96.7% and 95% respectively. MLLSV derived from post-processing procedure of DECT is useful in discriminating gout patients from healthy people.

Sparse-Denoising Methods for Extracting Desaturation Transients in Cerebral Oxygenation Signals of Preterm Infants.

Preterm infants are at high risk of developing brain injury in the first days of life as a consequence of poor cerebral oxygen delivery. Near-infrared spectroscopy (NIRS) is an established technology developed to monitor regional tissue oxygenation. Detailed waveform analysis of the cerebral NIRS signal could improve the clinical utility of this method in accurately predicting brain injury. Frequent transient cerebral oxygen desaturations are commonly observed in extremely preterm infants, yet their clinical significance remains unclear. The aim of this study was to examine and compare the performance of two distinct approaches in isolating and extracting transient deflections within NIRS signals. We optimized three different simultaneous low-pass filtering and total variation denoising (LPF-TVD) methods and compared their performance with a recently proposed method that uses singular-spectrum analysis and the discrete cosine transform (SSA-DCT). Parameters for the LPF-TVD methods were optimized over a grid search using synthetic NIRS-like signals. The SSA-DCT method was modified with a post-processing procedure to increase sparsity in the extracted components. Our analysis, using a synthetic NIRS-like dataset, showed that a LPF-TVD method outperformed the modified SSA-DCT method: median mean-squared error of 0.97 (95% CI: 0.86 to 1.07) was lower for the LPF-TVD method compared to the modified SSA-DCT method of 1.48 (95% CI: 1.33 to 1.63), P<0.001. The dual low-pass filter and total variation denoising methods are considerably more computational efficient, by 3 to 4 orders of magnitude, than the SSA-DCT method. More research is needed to examine the efficacy of these methods in extracting oxygen desaturation in real NIRS signals.Clinical relevance- Early and precise identification of abnormal brain oxygenation in premature infants would enable clinicians to employ therapeutic strategies that seek to prevent brain injury and long-term morbidity in this vulnerable population.

On posterior contraction of parameters and interpretability in Bayesian mixture modeling

We study posterior contraction behaviors for parameters of interest in the context of Bayesian mixture modeling, where the number of mixing components is unknown while the model itself may or may not be correctly specified. Two representative types of prior specification will be considered: one requires explicitly a prior distribution on the number of mixture components, while the other places a nonparametric prior on the space of mixing distributions. The former is shown to yield an optimal rate of posterior contraction on the model parameters under minimal conditions, while the latter can be utilized to consistently recover the unknown number of mixture components, with the help of a fast probabilistic post-processing procedure. We then turn the study of these Bayesian procedures to the realistic settings of model misspecification. It will be shown that the modeling choice of kernel density functions plays perhaps the most impactful roles in determining the posterior contraction rates in the misspecified situations. Drawing on concrete posterior contraction rates established in this paper we wish to highlight some aspects about the interesting tradeoffs between model expressiveness and interpretability that a statistical modeler must negotiate in the rich world of mixture modeling.

Separation of 44Sc from 44Ti in the Context of A Generator System for Radiopharmaceutical Purposes with the Example of [44Sc]Sc-PSMA-617 and [44Sc]Sc-PSMA-I&T Synthesis.

Today, 44Sc is an attractive radionuclide for molecular imaging with PET. In this work, we evaluated a 44Ti/44Sc radionuclide generator based on TEVA resin as a source of 44Sc. The generator prototype (5 MBq) exhibits high 44Ti retention and stable yield of 44Sc (91 ± 6 %) in 1 mL of eluate (20 bed volumes, eluent—0.1 M oxalic acid/0.2 M HCl) during one year of monitoring (more than 120 elutions). The breakthrough of 44Ti did not exceed 1.5 × 10−5% (average value was 6.5 × 10−6%). Post-processing of the eluate for further use in radiopharmaceutical synthesis was proposed. The post-processing procedure using a combination of Presep® PolyChelate and TK221 resins made it possible to obtain 44Sc-radioconjugates with high labeling yield (≥95%) while using small precursor amounts (5 nmol). The proposed method takes no more than 15 min and provides ≥90% yield relative to the 44Sc activity eluted from the generator. The labeling efficiency was demonstrated on the example of [44Sc]Sc-PSMA-617 and [44Sc]Sc-PSMA-I&T synthesis. Some superiority of PSMA-I&T over PSMA-617 in terms of 44Sc labeling efficiency was demonstrated (likely due to presence of DOTAGA chelator in the precursor structure). It was also shown that microwave heating of the reaction mixture considerably shortened the reaction time and improved radiolabeling yield and reproducibility of [44Sc]Sc-PSMA-617 and [44Sc]Sc-PSMA-I&T synthesis.