Multiscale Intensity Research Articles

Localization landscape of optical waves in multifractal photonic membranes

In this paper, we investigate the localization properties of optical waves in disordered systems with multifractal scattering potentials. In particular, we apply the localization landscape theory to the classical Helmholtz operator and, without solving the associated eigenproblem, show accurate predictions of localized eigenmodes for one- and two-dimensional multifractal structures. Finally, we design and fabricate nanoperforated photonic membranes in silicon nitride (SiN) and image directly their multifractal modes using leaky-mode spectroscopy in the visible spectral range. The measured data demonstrate optical resonances with multiscale intensity fluctuations in good qualitative agreement with numerical simulations. The proposed approach provides a convenient strategy to design multifractal photonic membranes, enabling rapid exploration of extended scattering structures with tailored disorder for enhanced light-matter interactions.

Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction

Addressing the issue of the simultaneous reconstruction of intensity and phase information in multiscale digital holography, an improved deep-learning model, Mimo-Net, is proposed. For holograms with uneven distribution of useful information, local feature extraction is performed to generate holograms of different scales, branch input training is used to realize multiscale feature learning, and feature information of different receptive fields is obtained. The up-sampling path outputs multiscale intensity and phase information simultaneously through dual channels. The experimental results show that compared to Y-Net, which is a network capable of reconstructing intensity and phase information simultaneously, Mimo-Net can perform intensity and phase reconstruction simultaneously on three different scales of holograms with only one training, improving reconstruction efficiency. The peak signal-to-noise ratio and structural similarity of the Mimo-Net reconstruction for three different scales of intensity and phase information are higher than those of the Y-Net reconstruction, improving the reconstruction performance.

Multi-Scale Variability of Coronal Loops Set by Thermal Non-Equilibrium and Instability as a Probe for Coronal Heating

Solar coronal loops are the building blocks of the solar corona. These dynamic structures are shaped by the magnetic field that expands into the solar atmosphere. They can be observed in X-ray and extreme ultraviolet (EUV), revealing the high plasma temperature of the corona. However, the dissipation of magnetic energy to heat the plasma to millions of degrees and, more generally, the mechanisms setting the mass and energy circulation in the solar atmosphere are still a matter of debate. Furthermore, multi-dimensional modelling indicates that the very concept of a coronal loop as an individual entity and its identification in EUV images is ill-defined due to the expected stochasticity of the solar atmosphere with continuous magnetic connectivity changes combined with the optically thin nature of the solar corona. In this context, the recent discovery of ubiquitous long-period EUV pulsations, the observed coronal rain properties and their common link in between represent not only major observational constraints for coronal heating theories but also major theoretical puzzles. The mechanisms of thermal non-equilibrium (TNE) and thermal instability (TI) appear in concert to explain these multi-scale phenomena as evaporation-condensation cycles. Recent numerical efforts clearly illustrate the specific but large parameter space involved in the heating and cooling aspects, and the geometry of the loop affecting the onset and properties of such cycles. In this review we will present and discuss this new approach into inferring coronal heating properties and understanding the mass and energy cycle based on the multi-scale intensity variability and cooling properties set by the TNE-TI scenario. We further discuss the major numerical challenges posed by the existence of TNE cycles and coronal rain, and similar phenomena at much larger scales in the Universe.

Multi-scale feature vector reconstruction for aircraft classification using high range resolution radar signatures

High-Resolution Range Profile (HRRP) is effective in various Radar Automatic Target Recognition problems. Multi-scale techniques have been verified in aircraft target HRRP feature enrichment to achieve aircraft recognition performance optimization. The involvement of multiple training-classification procedures in existing multi-scale methods results in tremendous resource consumption which challenges their real-time classification performance and application significance. This paper introduces a novel method that enables multi-scale HRRP features to be manipulated under a single scale for efficiency enhancement. Numerical analysis indicates that multi-scale intensity variations for particular HRRP scatterers could be modeled as Gaussian noises. Linear Discriminant Analysis and Singular Value Decomposition techniques are applied on reconstructed feature vectors for better separability and noise tolerance capability. Experimental results from dynamic aircraft recognition experiments verify the expected efficiency enhancement of the proposed method while maintaining comparable classification accuracy and better noise tolerance performance.

Intensity image quality assessment based on multiscale gradient magnitude similarity deviation

Laser active imaging is widely used in many fields. The intensity image quality of laser active imaging is affected by various degradations, such as speckle effect and noise. Most existing objective image quality assessment (IQA) methods that consider only a single distorted image are not suitable for an intensity image. A multiscale full-reference intensity IQA method is presented. The proposed method is based on an improved gradient magnitude similarity deviation (GMSD) in the nonsubsampled contourlet transform (NSCT) domain. The reference and distorted images are decomposed by NSCT to emulate the multichannel structure of the human visual system. Then, the GMSD of each sub-band is computed to capture the intensity image quality. At last, the contrast sensitivity function implementation is employed in the sub-bands of the NSCT domain. All sub-bands’ GMSD is evaluated and pooled together to yield the objective quality index of a distorted intensity image. Experimental results show that the proposed method can effectively and accurately evaluate the quality of intensity images, and it is highly consistent with subjective perception.

Automatic Repair of 3-D Neuron Reconstruction Based on Topological Feature Points and an MOST-Based Repairer

The digital reconstruction of neurons is essential to various neuroscientific studies. Due to the existence of gaps and ambiguities in neuron images, the neuron tracing results generated by most automatic reconstruction algorithms may be incomplete, resulting in false negatives (FNs), which need to be repaired in proof editing. However, the automatic proof-editing methods for repairing FN branches have rarely been explored. In this study, we propose a proof-editing algorithm for automatically detecting and repairing the FN branches of the initial reconstruction, which is based on a multiscale upgraded ray (MUR)-shooting model and an MOST-based repairer. The MUR detects the FN branch and the corresponding branch direction vector by analyzing the multiscale intensity distribution features around a topological feature point. The topological feature points contain the junction points detected from the neuron image and the tip nodes extracted from the initial reconstruction. The MOST-based repairer is proposed to prevent the redundant reconstructions by assigning the detected branch direction vector as the initial tracing direction, which rejects the nodes returning to the traced area. The experimental results demonstrate clearly that the proposed method can reduce 20% of the false-negative rate at most. The experimental results confirm that the proposed method is extremely helpful for generating faithful reconstructions.

Reflection intensity waveform inversion

Traditional iteration-based full-waveform inversion (FWI) methods encounter serious challenges if the initial velocity model is far from the true model or if the observed data are lacking low-frequency content. As such, the optimization algorithm may be trapped in local minima and fail to go to a global optimal model. In addition, the traditional FWI method requires long-offset data to update the deep structure of a velocity model with diving waves. To overcome the disadvantages of traditional FWI under these circumstances, we have developed a reflection intensity waveform inversion method. This method aims to minimize the seismic intensity differences between the modeled reflection data and field data. Our method is less dependent on the starting model, and long-offset data are no longer required. The wave intensity, proportional to the square of the original data amplitude, can have a low-frequency band and a higher frequency band, even for waveforms without initial low-frequency content. Our multiscale intensity inversion starts from the low-frequency information in the intensity data, and it can largely avoid the cycle-skipping problem. Synthetic and field data examples demonstrate that our method is able to overcome cycle skipping in handling data with no low-frequency information.

Depth Map Enhancement by Revisiting Multi-Scale Intensity Guidance Within Coarse-to-Fine Stages

Being different from the most methods of guided depth map enhancement based on deep convolutional neural network which focus on increasing the depth of networks, this paper is to improve the effectiveness of intensity guidance when the network goes deep. Overall, the proposed network upsamples the low-resolution depth maps from coarse to fine. Within each refinement stage of certain-scale depth features, the current-scale and all coarse-scales of the guidance features are revisited by dense connection. Therefore, the multi-scale guidance is efficiently maintained as the propagation of features. Furthermore, the proposed network maintains the intensity features in the high-resolution domain from which the multi-scale guidance is directly extracted. This design further improves the quality of intensity guidance. In addition, the shallow depth features upsampled via transposed convolution layer are directly transferred to the final depth features for reconstruction, which is called global residual learning in feature domain. Similarly, the global residual learning in pixel domain learns the difference between the depth ground truth and the coarsely upsampled depth map. Also, the local residual learning is to maintain the low frequency within each refinement stage and progressively recover the high frequency. The proposed method is tested for noise-free and noisy cases which compares against 16 state-of-the-art methods. Our experimental results show the improved performances based on the qualitative and quantitative evaluations.

Residual dense network for intensity-guided depth map enhancement

The depth maps captured by sensors always suffer from low resolution and random noise. Recently, by introducing the guidance from the color image, deep convolutional neural network (DCNN) shows significant improvements for depth map enhancement. However, most DCNN-based methods do not make full use of multi-scale guidance from the color image, thereby achieving sub-optimal performances. In this paper, we propose a novel DCNN to progressively reconstruct the high-resolution depth map guided by the intensity image. Specially, the multi-scale intensity features are extracted to provide guidance for the refinement of depth features as their resolutions are gradually enhanced. Furthermore, local residual learning and global residual learning are adopted in the output of each up-sampling sub-network and the whole network respectively. Such design can recover the high-frequency details from coarse to fine. In addition, according to the contiguous memory mechanism, the dense connections are designed to take the low-level features and the high-level features into account which further exploits the guidance from the intensity image. To balance the resource expense and the performance, the dimension reduction units are used to efficiently represent the features. The proposed network is compared with 17 state-of-the-art methods which shows improved performances.

Energy consumption and greenhouse gas emissions by buildings: A multi-scale perspective

Since the construction stage of buildings is heavily dependent on supports from urban, national and global economies, a hybrid systems analysis combining input-output analysis and process analysis is conducted in this work from a multi-scale perspective. Based on the multi-scale intensity databases, the embodied energy consumption and greenhouse gas emissions driven by the infrastructure engineering of case buildings in Beijing are systematically quantified at urban, national, and global scales with primary inputs in particular steel, cement, lime, and metal products. By tracing sources along the supply chains, the effects of building construction at the urban, national and global scales are calculated as 39.12%, 55.21%, and 5.67% of energy consumption and 68.78%, 24.41%, and 6.80% of greenhouse gas emissions, respectively. In general, basic materials (such as metal and petroleum) are mainly supplied by global scales, while synthetic materials (such as paints, fiber and rubber) are generally re-processed and transported within China. The energy consumption per area of the case buildings in E-town from urban, national and global scales are 3.03 GJ/m2, 4.27 GJ/m2 and 0.44 GJ/m2 respectively. Correspondingly, the related greenhouse gas emissions of the three scales are 0.40 t CO2-eq/m2, 0.14 t CO2-eq/m2 and 0.04 t CO2-eq/m2 respectively. With overall impacts of the construction engineering at urban, national, and global scales, the multi-entities responsibility assignments and tele-connected coordinate efforts for overall consumption and emission abatement could be derived from a multi-scale perspective.

A Multiscale Ray-Shooting Model for Termination Detection of Tree-Like Structures in Biomedical Images.

Digital reconstruction (tracing) of tree-like structures, such as neurons, retinal blood vessels, and bronchi, from volumetric images and 2D images is very important to biomedical research. Many existing reconstruction algorithms rely on a set of good seed points. The 2D or 3D terminations are good candidates for such seed points. In this paper, we propose an automatic method to detect terminations for tree-like structures based on a multiscale ray-shooting model and a termination visual prior. The multiscale ray-shooting model detects 2D terminations by extracting and analyzing the multiscale intensity distribution features around a termination candidate. The range of scale is adaptively determined according to the local neurite diameter estimated by the Rayburst sampling algorithm in combination with the gray-weighted distance transform. The termination visual prior is based on a key observation-when observing a 3D termination from three orthogonal directions without occlusion, we can recognize it in at least two views. Using this prior with the multiscale ray-shooting model, we can detect 3D terminations with high accuracies. Experiments on 3D neuron image stacks, 2D neuron images, 3D bronchus image stacks, and 2D retinal blood vessel images exhibit average precision and recall rates of 87.50% and 90.54%. The experimental results confirm that the proposed method outperforms other the state-of-the-art termination detection methods.

Patch-Based Label Fusion with Structured Discriminant Embedding for Hippocampus Segmentation.

Automatic and accurate segmentation of hippocampal structures in medical images is of great importance in neuroscience studies. In multi-atlas based segmentation methods, to alleviate the misalignment when registering atlases to the target image, patch-based methods have been widely studied to improve the performance of label fusion. However, weights assigned to the fused labels are usually computed based on predefined features (e.g. image intensities), thus being not necessarily optimal. Due to the lack of discriminating features, the original feature space defined by image intensities may limit the description accuracy. To solve this problem, we propose a patch-based label fusion with structured discriminant embedding method to automatically segment the hippocampal structure from the target image in a voxel-wise manner. Specifically, multi-scale intensity features and texture features are first extracted from the image patch for feature representation. Margin fisher analysis (MFA) is then applied to the neighboring samples in the atlases for the target voxel, in order to learn a subspace in which the distance between intra-class samples is minimized and the distance between inter-class samples is simultaneously maximized. Finally, the k-nearest neighbor (kNN) classifier is employed in the learned subspace to determine the final label for the target voxel. In the experiments, we evaluate our proposed method by conducting hippocampus segmentation using the ADNI dataset. Both the qualitative and quantitative results show that our method outperforms the conventional multi-atlas based segmentation methods.

Effective Pneumothorax Detection for Chest X-Ray Images Using Local Binary Pattern and Support Vector Machine.

Automatic image segmentation and feature analysis can assist doctors in the treatment and diagnosis of diseases more accurately. Automatic medical image segmentation is difficult due to the varying image quality among equipment. In this paper, the automatic method employed image multiscale intensity texture analysis and segmentation to solve this problem. In this paper, firstly, SVM is applied to identify common pneumothorax. Features are extracted from lung images with the LBP (local binary pattern). Then, classification of pneumothorax is determined by SVM. Secondly, the proposed automatic pneumothorax detection method is based on multiscale intensity texture segmentation by removing the background and noises in chest images for segmenting abnormal lung regions. The segmentation of abnormal regions is used for texture transformed from computing multiple overlapping blocks. The rib boundaries are identified with Sobel edge detection. Finally, in obtaining a complete disease region, the rib boundary is filled up and located between the abnormal regions.

Reduced-reference quality assessment of DIBR-synthesized images based on multi-scale edge intensity similarity

Depth-image-based-rendering (DIBR) plays an important role in view synthesis for free-viewpoint videos. The warping process in DIBR causes geometric displacement, which distributes intensively around edges, and the subsequent rendering process results in the impairment of edges. Traditional 2D image quality metrics are limited in the quality evaluation of DIBR-synthesized images. In this paper, we present a reduced-reference quality metric for DIBR-synthesized images by only extracting several feature values, namely multi-scale Edge Intensity Similarity (EIS). The original and synthesized images are first downsampled to generate images with different resolutions. Then an edge detection process is conducted on each scale and the edge intensity is calculated. The similarity of the edge intensity between each downsampled original image and the corresponding synthesized image is computed. Finally, the average similarity is calculated as the quality score of the DIBR-synthesized image. Experiments conducted on IRCCyN/IVC DIBR image and video databases demonstrate that the proposed method overall outperforms traditional 2D and existing DIBR-targeted quality metrics.

Estimation of Forest Topsoil Properties Using Airborne LiDAR-Derived Intensity and Topographic Factors

Forest topsoil supports vegetation growth and contains the majority of soil nutrients that are important indices of soil fertility and quality. Therefore, estimating forest topsoil properties, such as soil organic matter (SOM), total nitrogen (Total N), pH, litter-organic (O-A) horizon depth (Depth) and available phosphorous (AvaP), is of particular importance for forest development and management. As an emerging technology, light detection and ranging (LiDAR) can capture the three-dimensional structure and intensity information of scanned objects, and can generate high resolution digital elevation models (DEM) using ground echoes. Moreover, great power for estimating forest topsoil properties is enclosed in the intensity information of ground echoes. However, the intensity has not been well explored for this purpose. In this study, we collected soil samples from 62 plots and the coincident airborne LiDAR data in a Korean pine forest in Northeast China, and assessed the effectiveness of both multi-scale intensity data and LiDAR-derived topographic factors for estimating forest topsoil properties. The results showed that LiDAR-derived variables could be robust predictors of four topsoil properties (SOM, Total N, pH, and Depth), with coefficients of determination (R2) ranging from 0.46 to 0.66. Ground-returned intensity was identified as the most effective predictor for three topsoil properties (SOM, Total N, and Depth) with R2 values of 0.17–0.64. Meanwhile, LiDAR-derived topographic factors, except elevation and sediment transport index, had weak explanatory power, with R2 no more than 0.10. These findings suggest that the LiDAR intensity of ground echoes is effective for estimating several topsoil properties in forests with complicated topography and dense canopy cover. Furthermore, combining intensity and multi-scale LiDAR-derived topographic factors, the prediction accuracies (R2) were enhanced by negligible amounts up to 0.40, relative to using intensity only for topsoil properties. Moreover, the prediction accuracy for Depth increased by 0.20, while for other topsoil properties, the prediction accuracies increased negligibly, when the scale dependency of soil–topography relationship was taken into consideration.

A Novel Multiscale Intensity Metric for Evaluation of Tropical Cyclone Intensity Forecasts

Abstract In this study, a new multiscale intensity (MSI) metric for evaluating tropical cyclone (TC) intensity forecasts is presented. The metric consists of the resolvable and observable, low-wavenumber intensity represented by the sum of amplitudes of azimuthal wavenumbers 0 and 1 for wind speed within the TC vortex at the radius of maximum wind and a stochastic residual, all determined at 10-m elevation. The residual wind speed is defined as the difference between an estimate of maximum speed and the low-wavenumber intensity. The MSI metric is compared to the standard metric that includes only the maximum speed. Using stepped-frequency microwave radiometer wind speed observations from TC aircraft reconnaissance to estimate the low-wavenumber intensity and the National Hurricane Center’s best-track (BT) intensity for the maximum wind speed estimate, it is shown that the residual intensity is well represented as a stochastic quantity with small mean, standard deviation, and absolute norm values that are within the expected uncertainty of the BT estimates. The result strongly suggests that the practical predictability of TC intensity is determined by the observable and resolvable low-wavenumber intensity within the vortex. Verification of a set of high-resolution numerical forecasts using the MSI metric demonstrates that this metric provides more informative and more realistic estimates of the intensity forecast errors. It is also shown that the maximum speed metric allows for error compensation between the low-wavenumber and residual intensities, which could lead to forecast skill overestimation and inaccurate assessment of the impact of forecast system change on the skill.

Multiscale analysis of neural spike trains

This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code.

LGOH-Based Discriminant Centre-Surround Saliency Detection

Discriminant saliency is a kind of decision-theoretic-based saliency detection method that has been proposed recently. Based on local gradient distribution, this paper proposes a simple but efficient discriminant centre-surround hypothesis, and builds local and global saliency models by combining multi-scale intensity contrast with colour and orientation features. This method makes three important contributions. First, a circular and multi-scale hierarchical centre-surround profile is designed for the local saliency detection. Secondly, the dense local gradient orientation histogram (LGOH) of the centre-surround region is counted and used for the local saliency analysis. And thirdly, a new integration strategy for the local and global saliency is proposed and applied to the final visual saliency discriminant. Experiments demonstrate the effectiveness of the proposed method. Compared with 12 state-of-the-art saliency detection models, the proposed method outperforms the others in precision-recall, F-measures and mean absolute error (MAE), and can produce a more complete salient object.

Multiscale Intensity Models and Name Grouping for Valuation of Multi-Name Credit Derivatives

The pricing of collateralized debt obligations (CDOs) and other basket credit derivatives is contingent upon (i) a realistic modelling of the firms' default times and the correlation between them, and (ii) efficient computational methods for computing the portfolio loss distribution from the individual firms' default time distributions. Factor models, a widely used class of pricing models, are computationally tractable despite the large dimension of the pricing problem, thus satisfying issue (ii), but to have any hope of calibrating CDO data, numerically intense versions of these models are required. We revisit the intensity-based modelling setup for basket credit derivatives and, with the aforementioned issues in mind, we propose improvements (a) via incorporating fast mean-reverting stochastic volatility in the default intensity processes, and (b) by considering homogeneous groups within the original set of firms. This can be thought of as a hybrid of top-down and bottom-up approaches. We present a calibration example, including data in the midst of the 2008 financial credit crisis, and discuss the relative performance of the framework.

Homogeneous groups and multiscale intensity models for multiname credit derivatives

AbstractWe discuss a computationally tractable approach to the valuation of multiname credit derivatives employing name grouping for dimension reduction, and singular perturbation approximations for model robustness. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)