Reconstruction Fidelity Research Articles

The Potential of Deriving Tree-Ring-Based Field Reconstructions of Droughts and Pluvials over Fennoscandia*,+

Abstract Moisture availability has been identified as one of the most important factors in the context of future climate change. This paper explores the potential of applying a multiproxy approach to dendroclimatology to infer the twentieth-century moisture variability over Fennoscandia. Fields of the warm-season (June–August) standardized precipitation evapotranspiration index (SPEI) were developed from a dense network of precipitation-sensitive annually resolved tree-ring width (TRW), maximum density (MXD), and stable carbon (δ13C) and oxygen (δ18O) isotope chronologies using a point-by-point local regression technique (PPR). Two different approaches were tested for selecting candidate tree-ring predictors of SPEI for each gridpoint reconstruction: a search radius method and a search spatial correlation contour method. As confirmed by a range of metrics of reconstruction fidelity, both methods produced reconstructions showing a remarkably high accuracy in a temporal sense, but with some minor regional differences. As a whole, the spatial skill of the reconstructed fields was generally quite good, showing the greatest performance in the central and southern parts of the target region. Lower reconstruction skills were observed in northern part of the study domain. Regional-scale moisture anomalies were best captured by the reconstructions, while local-scale features were not as well represented. The authors speculate that a spatially and temporally varying tree-ring proxy response to temperature and precipitation in the region may cause some uncertainties in a Fennoscandian hydroclimatic reconstruction; this needs further investigation. Overall, this study shows a great potential for making long-term spatiotemporal reconstructions of moisture variability for the Fennoscandian region using tree-ring data.

MATHEMATICAL MODELING IN INDUSTRIAL TOMOGRAPHIC DIAGNOSIS WITH INCOMPLETE PROJECTION DATA

In this article it has been proposed to use the analog of the Pelly-Wiener theorem; the theorem allows to synthesize the unknown projections using known projections. Within the research the special requirements were collected and analyzed, on basis of which the program for modeling sinogram obtaining process, calculating the coefficients of a homogeneous polynomial, examining the fidelity of the object reconstruction was projected and realized using coefficients and actual data.

Removing the effects of the “dark matter” in tomography

Electron tomography (ET) using different imaging modes has been progressively consolidating its position as a key tool in materials science. The fidelity of a tomographic reconstruction, or tomogram, is affected by several experimental factors. Most often, an unrealistic cloud of intensity that does not correspond to a real material phase of the specimen (“dark matter”) blurs the tomograms and enhances artefacts arising from the missing wedge (MW). Here we show that by simple preprocessing of the background level of any tomographic tilt series, it is possible to minimise the negative effects of that “dark matter”. Iterative reconstruction algorithms converge better, leading to tomograms with fewer streaking artefacts from the MW, more contrast, and increased accuracy. The conclusions are valid irrespective of the imaging mode used, and the methodology improves the segmentation and visualisation of tomograms of both crystalline and amorphous materials. We show examples of HAADF STEM and BF TEM tomography.

Aberration compensation of holographic particle images using digital holographic microscopy

Characterisation of small and large-scale vortices in turbulent flows demands a system with high spatial resolution. The measurement of high spatial resolution, three-dimensional vector displacements in fluid mechanics using holography, is usually hampered by aberration. Aberration poses some problems in particle image identification due to low fidelity of real image reconstruction. Phase mismatch between the recording and the reconstruction waves was identified as the main source of aberration in this study. This paper demonstrates how aberration compensation can be achieved by cross-correlating the complex amplitude of an aberrated reconstructed object with the phase conjugate of a known reference object in the plane of the hologram (frequency space). Results favourably show significant increase in Strehl ratio and suppression of background noise that are more pronounced for particle images of 10 and 5 microns. It is clear from the work conducted that wavefront aberration measurement and compensation of holographic microscopic objects are now possible with the use of a variant digital holographic microscope.

Optimal sampling with prior information of the image geometry in microfluidic MRI

Recent advances in MRI acquisition for microscopic flows enable unprecedented sensitivity and speed in a portable NMR/MRI microfluidic analysis platform. However, the application of MRI to microfluidics usually suffers from prolonged acquisition times owing to the combination of the required high resolution and wide field of view necessary to resolve details within microfluidic channels. When prior knowledge of the image geometry is available as a binarized image, such as for microfluidic MRI, it is possible to reduce sampling requirements by incorporating this information into the reconstruction algorithm. The current approach to the design of the partial weighted random sampling schemes is to bias toward the high signal energy portions of the binarized image geometry after Fourier transformation (i.e. in its k-space representation). Although this sampling prescription is frequently effective, it can be far from optimal in certain limiting cases, such as for a 1D channel, or more generally yield inefficient sampling schemes at low degrees of sub-sampling. This work explores the tradeoff between signal acquisition and incoherent sampling on image reconstruction quality given prior knowledge of the image geometry for weighted random sampling schemes, finding that optimal distribution is not robustly determined by maximizing the acquired signal but from interpreting its marginal change with respect to the sub-sampling rate. We develop a corresponding sampling design methodology that deterministically yields a near optimal sampling distribution for image reconstructions incorporating knowledge of the image geometry. The technique robustly identifies optimal weighted random sampling schemes and provides improved reconstruction fidelity for multiple 1D and 2D images, when compared to prior techniques for sampling optimization given knowledge of the image geometry.

Model cerebellar granule cells can faithfully transmit modulated firing rate signals.

A crucial assumption of many high-level system models of the cerebellum is that information in the granular layer is encoded in a linear manner. However, granule cells are known for their non-linear and resonant synaptic and intrinsic properties that could potentially impede linear signal transmission. In this modeling study we analyse how electrophysiological granule cell properties and spike sampling influence information coded by firing rate modulation, assuming no signal-related, i.e., uncorrelated inhibitory feedback (open-loop mode). A detailed one-compartment granule cell model was excited in simulation by either direct current or mossy-fiber synaptic inputs. Vestibular signals were represented as tonic inputs to the flocculus modulated at frequencies up to 20 Hz (approximate upper frequency limit of vestibular-ocular reflex, VOR). Model outputs were assessed using estimates of both the transfer function, and the fidelity of input-signal reconstruction measured as variance-accounted-for. The detailed granule cell model with realistic mossy-fiber synaptic inputs could transmit information faithfully and linearly in the frequency range of the vestibular-ocular reflex. This was achieved most simply if the model neurons had a firing rate at least twice the highest required frequency of modulation, but lower rates were also adequate provided a population of neurons was utilized, especially in combination with push-pull coding. The exact number of neurons required for faithful transmission depended on the precise values of firing rate and noise. The model neurons were also able to combine excitatory and inhibitory signals linearly, and could be replaced by a simpler (modified) integrate-and-fire neuron in the case of high tonic firing rates. These findings suggest that granule cells can in principle code modulated firing-rate inputs in a linear manner, and are thus consistent with the high-level adaptive-filter model of the cerebellar microcircuit.

Analysis of four-dimensional Mie imaging using fiber-based endoscopes

This work reports the demonstration and analysis of four-dimensional (4D) imaging measurements in two-phase flows using fiber-based endoscopes (FBEs). Such 4D measurements resolve the droplet distribution in two-phase flows in all three spatial directions and with a temporal resolution of up to 5 kHz. Demonstration measurements were performed in a measurement volume of 85 mm × 85 mm × 85 mm discretized into 64 × 64 × 64 voxels to illustrate FBEs' potential for facilitating practical implementation of 4D tomographic measurements. Mathematical analyses were performed to quantify the fundamental advantage of FBEs to enhance the reconstruction fidelity.

The properties of SIRT, TVM, and DART for 3D imaging of tubular domains in nanocomposite thin-films and sections

In electron tomography, the fidelity of the 3D reconstruction strongly depends on the employed reconstruction algorithm. In this paper, the properties of SIRT, TVM and DART reconstructions are studied with respect to having only a limited number of electrons available for imaging and applying different angular sampling schemes. A well-defined realistic model is generated, which consists of tubular domains within a matrix having slab-geometry. Subsequently, the electron tomography workflow is simulated from calculated tilt-series over experimental effects to reconstruction. In comparison with the model, the fidelity of each reconstruction method is evaluated qualitatively and quantitatively based on global and local edge profiles and resolvable distance between particles. Results show that the performance of all reconstruction methods declines with the total electron dose. Overall, SIRT algorithm is the most stable method and insensitive to changes in angular sampling. TVM algorithm yields significantly sharper edges in the reconstruction, but the edge positions are strongly influenced by the tilt scheme and the tubular objects become thinned. The DART algorithm markedly suppresses the elongation artifacts along the beam direction and moreover segments the reconstruction which can be considered a significant advantage for quantification. Finally, no advantage of TVM and DART to deal better with fewer projections was observed.

Inter-Relay Cooperation: A New Paradigm for Enhanced Relay-Assisted FSO Communications

In this paper, we study the effect of the presence of inter-relay connections on the performance of cooperative free-space optical (FSO) networks. Unlike the existing literature where conventional cooperation in FSO systems is realized in two steps through source-relay (S-R) followed by relay-destination (R-D) communications, we propose and analyze a novel three-stage cooperation methodology. The proposed scheme is based on exploiting the potential presence of FSO links between neighboring relays for the sake of complementing the S-R and R-D steps with an intermediate inter-relay communication step that serves in enhancing the fidelity of signal reconstruction at the relays before retransmitting to the destination. For the proposed scheme, we derive closed-form expressions for the conditional error probability with any number of relays. We also propose a closed-form power allocation strategy (PAS) in the case where the background noise is negligible. This PAS is based on minimizing an asymptotic upper-bound on the conditional error probability by applying the method of Lagrange multipliers with the solution satisfying the Karush-Kuhn-Tucker (KKT) conditions. Results show that the presence of inter-relay connections, associated with an appropriate cooperation scheme and PAS, can significantly boost the performance of relay-assisted FSO systems.

SU‐E‐J‐149: Establishing the Relationship Between Pre‐Treatment Lung Ventilation, Dose, and Toxicity Outcome

Purpose:Recently, there has been an interest in incorporating functional information in treatment planning especially in thoracic tumors. The rationale is that healthy lung regions need to be spared from radiation if possible to help achieve better control on toxicity. However, it is still unclear whether high functioning regions need to be spared or have more capacity to deal with the excessive radiation as compared to the compromised regions of the lung. Our goal with this work is to establish the tools by which we can establish a relationship between pre‐treatment lung function, dose, and radiographic outcomes of lung toxicity.Methods:Treatment planning was performed using a single phase of a 4DCT scan, and follow‐up anatomical CT scans were performed every 3 months for most patients. In this study, we developed the pipeline of tools needed to analyze such a large dataset, while trying to establish a relationship between function, dose, and outcome. Pre‐treatment lung function was evaluated using a recently published technique that evaluates Fractional Regional Ventilation (FRV). All images including the FRV map and the individual follow‐up anatomical CT images were all spatially matched to the planning CT using a diffusion based Demons image registration algorithm. Change in HU value was used as a metric to capture the effects of lung toxicity. To validate the findings, a radiologist evaluated the follow‐up anatomical CT images and scored lung toxicity.Results:Initial experience in 1 patient shows a relationship between the pre‐treatment lung function, dose and toxicity outcome. The results are also correlated to the findings by the radiologist who was blinded to the analysis or dose.Conclusion:The pipeline we have established to study this enables future studies in large retrospective studies. However, the tools are dependent on the fidelity of 4DCT reconstruction for accurate evaluation of regional ventilation.Patent Pending for the technique presented in this work to evaluate FRV incorporating mass correction.

3D reconstruction methods for digital preservation of cultural heritage: A survey

3D reconstruction, refers to capturing and reproducing the shape and appearance of an arbitrary object or scene given depth and color information. This is a broad research area within the computer vision field involving many stages and still open problems. The digital preservation of cultural heritage is a specially challenging application of 3D reconstruction. Cultural heritage objects and sites greatly differ from each other and a maximized fidelity of the 3D reconstruction is a core requirement. The literature on this topic has substantially increased in the past years, mostly due to the variety of scenarios and the development of new depth sensing devices as well as techniques able to deal with this issue. In our search to develop a complete 3D reconstruction pipeline, we have comprehensively studied techniques related to this topic and divided the 3D digitization process in four major overviews: image acquisition, view registration, mesh integration and texture generation. We present the state-of-the-art approaches and challenges of each stage.

Energy efficient telemonitoring of physiological signals via compressed sensing: A fast algorithm and power consumption evaluation

Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usually compressed before transmission. In this paper, we adopt compressed sensing (CS) as a low-power compression framework, and propose a fast block sparse Bayesian learning (BSBL) algorithm to reconstruct original signals. Experiments on real-world fetal ECG signals and epilepsy EEG signals showed that the proposed algorithm has good balance between speed and data reconstruction fidelity when compared to state-of-the-art CS algorithms. Further, we implemented the CS-based compression procedure and a low-power compression procedure based on a wavelet transform in field programmable gate array (FPGA), showing that the CS-based compression can largely save energy and other on-chip computing resources.

Fidelity and repeatability of wave fields reconstructed from multicomponent streamer data

ABSTRACTWave field reconstruction – the estimation of a three‐dimensional (3D) wave field representing upgoing, downgoing or the combined total pressure at an arbitrary point within a marine streamer array – is enabled by simultaneous measurements of the crossline and vertical components of particle acceleration in addition to pressure in a multicomponent marine streamer. We examine a repeated sail line of North Sea data acquired by a prototype multicomponent towed‐streamer array for both wave field reconstruction fidelity (or accuracy) and reconstruction repeatability. Data from six cables, finely sampled in‐line but spaced at 75 m crossline, are reconstructed and placed on a rectangular data grid uniformly spaced at 6.25 m in‐line and crossline. Benchmarks are generated using recorded pressure data and compared with wave fields reconstructed from pressure alone, and from combinations of pressure, crossline acceleration and vertical acceleration. We find that reconstruction using pressure and both crossline and vertical acceleration has excellent fidelity, recapturing highly aliased diffractions that are lost by interpolation of pressure‐only data. We model wave field reconstruction error as a linear function of distance from the nearest physical sensor and find, for this data set with some mismatched shot positions, that the reconstructed wave field error sensitivity to sensor mispositioning is one‐third that of the recorded wave field sensitivity. Multicomponent reconstruction is also more repeatable, outperforming single‐component reconstruction in which wave field mismatch correlates with geometry mismatch. We find that adequate repeatability may mask poor reconstruction fidelity and that aliased reconstructions will repeat if the survey geometry repeats. Although the multicomponent 3D data have only 500 m in‐line aperture, limiting the attenuation of non‐repeating multiples, the level of repeatability achieved is extremely encouraging compared to full‐aperture, pressure‐only, time‐lapse data sets at an equivalent stage of processing.

Sparse weightings for collapsing inverse solutions to cortical parcellations optimize M/EEG source reconstruction accuracy

Source-reconstructed magneto- and electroencephalography (M/EEG) are promising tools for investigating the human functional connectome. To reduce data, decrease noise, and obtain results directly comparable to magnetic resonance imaging (MRI), M/EEG source data can be collapsed into a cortical parcellation. For most collapsing approaches, however, it remains unclear if collapsed parcel time series accurately represent the coherent source dynamics within each parcel. We introduce a collapse-weighting-operator optimization approach that maximizes parcel fidelity, i.e., the phase correlation between original source dynamics and collapsed parcel time series, and thereby the accuracy with which the source dynamics are retained in forward and inverse modeling. The sparse, optimized weighting operator increased parcel fidelity 57-73% and true positive rate of interaction mapping from 0.33 to 0.84 in comparison to a non-sparse weighting approach. These improvements were robust for variable source topographies and parcellation resolutions. Critically, in real inverse-modeled MEG data, the optimized operator yielded close-to-perfect intra-parcel coherence. Previous suggestions for obtaining parcel time series include averaging all source time series within each anatomical parcel or using exclusively the time series of the voxel with maximum power. These methods are sensitive to signal heterogeneity and outlier sources. The approach advanced here avoids these problems. The optimized operator is suitable for collapsing real source-reconstructed M/EEG data into any cortical parcellation. The enhanced time series reconstruction fidelity yields improved accuracy of subsequent analyses of both local dynamics and large-scale interaction mapping.

Evaluating climate field reconstruction techniques using improved emulations of real-world conditions

Abstract. Pseudoproxy experiments (PPEs) have become an important framework for evaluating paleoclimate reconstruction methods. Most existing PPE studies assume constant proxy availability through time and uniform proxy quality across the pseudoproxy network. Real multiproxy networks are, however, marked by pronounced disparities in proxy quality, and a steep decline in proxy availability back in time, either of which may have large effects on reconstruction skill. A suite of PPEs constructed from a millennium-length general circulation model (GCM) simulation is thus designed to mimic these various real-world characteristics. The new pseudoproxy network is used to evaluate four climate field reconstruction (CFR) techniques: truncated total least squares embedded within the regularized EM (expectation-maximization) algorithm (RegEM-TTLS), the Mann et al. (2009) implementation of RegEM-TTLS (M09), canonical correlation analysis (CCA), and Gaussian graphical models embedded within RegEM (GraphEM). Each method's risk properties are also assessed via a 100-member noise ensemble. Contrary to expectation, it is found that reconstruction skill does not vary monotonically with proxy availability, but also is a function of the type and amplitude of climate variability (forced events vs. internal variability). The use of realistic spatiotemporal pseudoproxy characteristics also exposes large inter-method differences. Despite the comparable fidelity in reconstructing the global mean temperature, spatial skill varies considerably between CFR techniques. Both GraphEM and CCA efficiently exploit teleconnections, and produce consistent reconstructions across the ensemble. RegEM-TTLS and M09 appear advantageous for reconstructions on highly noisy data, but are subject to larger stochastic variations across different realizations of pseudoproxy noise. Results collectively highlight the importance of designing realistic pseudoproxy networks and implementing multiple noise realizations of PPEs. The results also underscore the difficulty in finding the proper bias-variance tradeoff for jointly optimizing the spatial skill of CFRs and the fidelity of the global mean reconstructions.

Photogrammetry for use in biological surface acquisition: investigation of use, geometric accuracy and consequence on analysis

Medical imaging has advanced medical and scientific understanding of disease since its implementation. However, a wealth of information that may be beneficial for detailed surgical review, surgical-specific device design and retrospective case studies is not gathered. In this study, a single-camera photogrammetry method is proposed to investigate a feasible method of capturing exposed in vivo geometric data in an open surgical environment. To address current restrictions, the method must be rapid, cost-effective and not an impede surgical protocol performance. The tool is validated for geometry reconstruction fidelity against an anatomically complex model of known dimensions using three test criteria: geometry, structural simulation and fluid simulation. Each reconstruction was subject to three levels of smoothing to test solution sensitivity and feature erosion. The model was reconstructed three times to test variability of the reconstruction. All reconstructions were carried out using a freely available webservice. The models were geometrically accurate, with half of the model area below 1% error (0.5 mm) and 75–84% area below 2% error (1 mm). Finite element analysis and computational fluid dynamics simulations carried out found that over 84% and 90% of the model, for both the structural and fluid simulations, were within 10% of the control value, respectively. The results and conclusions reported illustrate the capability of using photogrammetry for geometric reconstruction and computational analysis of in vivo geometries.

Revisiting the spread spectrum effect in radio interferometric imaging: a sparse variant of the w-projection algorithm

Next-generation radio interferometric telescopes will exhibit non-coplanar baseline configurations and wide field-of-views, inducing a w-modulation of the sky image, which in turn induces the spread spectrum effect. We revisit the impact of this effect on imaging quality and study a new algorithmic strategy to deal with the associated operator in the image reconstruction process. In previous studies it has been shown that image recovery in the framework of compressed sensing is improved due to the spread spectrum effect, where the w-modulation can act to increase the incoherence between measurement and sparsifying signal representations. For the purpose of computational efficiency, idealised experiments were performed, where only a constant baseline component w in the pointing direction of the telescope was considered. We extend this analysis to the more realistic setting where the w-component varies for each visibility measurement. Firstly, incorporating varying w-components into imaging algorithms is a computational demanding task. We propose a variant of the w-projection algorithm for this purpose, which is based on an adaptive sparsification procedure, and incorporate it in compressed sensing imaging methods. This sparse matrix variant of the w-projection algorithm is generic and adapts to the support of each kernel. Consequently, it is applicable for all types of direction-dependent effects. Secondly, we show that for w-modulation with varying w-components, reconstruction quality is significantly improved compared to the setting where there is no w-modulation (i.e. w=0), reaching levels comparable to the quality of a constant, maximal w-component. This finding confirms that one may seek to optimise future telescope configurations to promote large w-components, thus enhancing the spread spectrum effect and consequently the fidelity of image reconstruction.

Distributed Sensing and Transmission of Sporadic Random Samples Over a Multiple-Access Channel

This work considers distributed sensing and transmission of sporadic random samples. Lower bounds are derived for the reconstruction error of a single normally or uniformly-distributed finite-dimensional vector imperfectly measured by a network of sensors and transmitted with finite energy to a common receiver via an additive white Gaussian noise asynchronous multiple-access channel. Transmission makes use of a perfect causal feedback link to the encoder connected to each sensor. A retransmission protocol inspired by the classical scheme in [1] applied to the transmission of single and bi-variate analog samples analyzed in [2] and [3] is extended to the more general network scenario, for which asymptotic upper-bounds on the reconstruction error are provided. Both the upper and lower-bounds show that collaboration can be achieved through energy accumulation under certain circumstances. In order to investigate the practical performance of the proposed retransmission protocol we provide a numerical evaluation of the upper-bounds in the non-asymptotic energy regime using low-order quantization in the sensors. The latter includes a minor modification of the protocol to improve reconstruction fidelity. Numerical results show that an increase in the size of the network brings benefit in terms of performance, but that the gain in terms of energy efficiency diminishes quickly at finite energies due to a non-coherent combining loss.

Transductive face sketch-photo synthesis.

Face sketch-photo synthesis plays a critical role in many applications, such as law enforcement and digital entertainment. Recently, many face sketch-photo synthesis methods have been proposed under the framework of inductive learning, and these have obtained promising performance. However, these inductive learning-based face sketch-photo synthesis methods may result in high losses for test samples, because inductive learning minimizes the empirical loss for training samples. This paper presents a novel transductive face sketch-photo synthesis method that incorporates the given test samples into the learning process and optimizes the performance on these test samples. In particular, it defines a probabilistic model to optimize both the reconstruction fidelity of the input photo (sketch) and the synthesis fidelity of the target output sketch (photo), and efficiently optimizes this probabilistic model by alternating optimization. The proposed transductive method significantly reduces the expected high loss and improves the synthesis performance for test samples. Experimental results on the Chinese University of Hong Kong face sketch data set demonstrate the effectiveness of the proposed method by comparing it with representative inductive learning-based face sketch-photo synthesis methods.

Sparse Image Reconstruction on the Sphere: Implications of a New Sampling Theorem

We study the impact of sampling theorems on the fidelity of sparse image reconstruction on the sphere. We discuss how a reduction in the number of samples required to represent all information content of a band-limited signal acts to improve the fidelity of sparse image reconstruction, through both the dimensionality and sparsity of signals. To demonstrate this result, we consider a simple inpainting problem on the sphere and consider images sparse in the magnitude of their gradient. We develop a framework for total variation inpainting on the sphere, including fast methods to render the inpainting problem computationally feasible at high resolution. Recently a new sampling theorem on the sphere was developed, reducing the required number of samples by a factor of two for equiangular sampling schemes. Through numerical simulations, we verify the enhanced fidelity of sparse image reconstruction due to the more efficient sampling of the sphere provided by the new sampling theorem.