Speckle Features Research Articles

Large-scale photonic computing with nonlinear disordered media.

Neural networks find widespread use in scientific and technological applications, yet their implementations in conventional computers have encountered bottlenecks due to ever-expanding computational needs. Photonic computing is a promising neuromorphic platform with potential advantages of massive parallelism, ultralow latency and reduced energy consumption but mostly for computing linear operations. Here we demonstrate a large-scale, high-performance nonlinear photonic neural system based on a disordered polycrystalline slab composed of lithium niobate nanocrystals. Mediated by random quasi-phase-matching and multiple scattering, linear and nonlinear optical speckle features are generated as the interplay between the simultaneous linear random scattering and the second-harmonic generation, defining a complex neural network in which the second-order nonlinearity acts as internal nonlinear activation functions. Benchmarked against linear random projection, such nonlinear mapping embedded with rich physical computational operations shows improved performance across a large collection of machine learning tasks in image classification, regression and graph classification. Demonstrating up to 27,648 input and 3,500 nonlinear output nodes, the combination of optical nonlinearity and random scattering serves as a scalable computing engine for diverse applications.

Quantification of attenuation and speckle features from endoscopic OCT images for the diagnosis of human brain glioma

Application of optical coherence tomography (OCT) in neurosurgery mostly includes the discrimination between intact and malignant tissues aimed at the detection of brain tumor margins. For particular tissue types, the existing approaches demonstrate low performance, which stimulates the further research for their improvement. The analysis of speckle patterns of brain OCT images is proposed to be taken into account for the discrimination between human brain glioma tissue and intact cortex and white matter. The speckle properties provide additional information of tissue structure, which could help to increase the efficiency of tissue differentiation. The wavelet analysis of OCT speckle patterns was applied to extract the power of local brightness fluctuations in speckle and its standard deviation. The speckle properties are analysed together with attenuation ones using a set of ex vivo brain tissue samples, including glioma of different grades. Various combinations of these features are considered to perform linear discriminant analysis for tissue differentiation. The results reveal that it is reasonable to include the local brightness fluctuations at first two wavelet decomposition levels in the analysis of OCT brain images aimed at neurosurgical diagnosis.

Theoretical and experimental investigations of speckle features based on free-space surface scattering

Speckle is a significant challenge for laser imaging systems, as it degrades the image quality. In this study, an improved theoretical model is established to describe the speckle features in free-space optical path. The model quantitatively defines the relationship between speckle contrast and five parameters: wavelength, screen surface roughness, light-spot diameter, incidence angle, and observation angle. Subsequently, the theoretical results are experimentally verified. This study enhances the theory of speckle suppression in free-space optical path, and thus enriches the existing speckle suppression theory. The speckle features based on free-space surface-scattered fields have the potential for applications related to non-imaging optics.

A Region-Adaptive Local Perturbation-Based Method for Generating Adversarial Examples in Synthetic Aperture Radar Object Detection

In synthetic aperture radar (SAR) imaging, intelligent object detection methods are facing significant challenges in terms of model robustness and application security, which are posed by adversarial examples. The existing adversarial example generation methods for SAR object detection can be divided into two main types: global perturbation attacks and local perturbation attacks. Due to the dynamic changes and irregular spatial distribution of SAR coherent speckle backgrounds, the attack effectiveness of global perturbation attacks is significantly reduced by coherent speckle. In contrast, by focusing on the image objects, local perturbation attacks achieve targeted and effective advantages over global perturbations by minimizing interference from the SAR coherent speckle background. However, the adaptability of conventional local perturbations is limited because they employ a fixed size without considering the diverse sizes and shapes of SAR objects under various conditions. This paper presents a framework for region-adaptive local perturbations (RaLP) specifically designed for SAR object detection tasks. The framework consists of two modules. To address the issue of coherent speckle noise interference in SAR imagery, we develop a local perturbation generator (LPG) module. By filtering the original image, this module reduces the speckle features introduced during perturbation generation. It then superimposes adversarial perturbations in the form of local perturbations on areas of the object with weaker speckles, thereby reducing the mutual interference between coherent speckles and adversarial perturbation. To address the issue of insufficient adaptability in terms of the size variation in local adversarial perturbations, we propose an adaptive perturbation optimizer (APO) module. This optimizer adapts the size of the adversarial perturbations based on the size and shape of the object, effectively solving the problem of adaptive perturbation size and enhancing the universality of the attack. The experimental results show that RaLP reduces the detection accuracy of the YOLOv3 detector by 29.0%, 29.9%, and 32.3% on the SSDD, SAR-Ship, and AIR-SARShip datasets, respectively, and the model-to-model and dataset-to-dataset transferability of RaLP attacks are verified.

NMSCANet: stereo matching network for speckle variations in single-shot speckle projection profilometry.

In single-shot speckle projection profilometry (SSPP), the projected speckle inevitably undergoes changes in shape and size due to variations such as viewing angles, complex surface modulations of the test object and different projection ratios. These variations introduce randomness and unpredictability to the speckle features, resulting in erroneous or missing feature extraction and subsequently degrading 3D reconstruction accuracy across the tested surface. This work strives to explore the relationship between speckle size variations and feature extraction, and address the issue solely from the perspective of network design by leveraging specific variations in speckle size without expanding the training set. Based on the analysis of the relationship between speckle size variations and feature extraction, we introduce the NMSCANet, enabling the extraction of multi-scale speckle features. Multi-scale spatial attention is employed to enhance the perception of complex and varying speckle features in space, allowing comprehensive feature extraction across different scales. Channel attention is also employed to selectively highlight the most important and representative feature channels in each image, which is able to enhance the detection capability of high-frequency 3D surface profiles. Especially, a real binocular 3D measurement system and its digital twin with the same calibration parameters are established. Experimental results imply that NMSCANet can also exhibit more than 8 times the point cloud reconstruction stability (Std) on the testing set, and the smallest change range in terms of Mean~dis (0.0614 mm - 0.4066 mm) and Std (0.0768 mm - 0.7367 mm) when measuring a standard sphere and plane compared to other methods, faced with the speckle size changes, meanwhile NMSCANet boosts the disparity matching accuracy (EPE) by over 35% while reducing the matching error (N-PER) by over 62%. Ablation studies and validity experiments collectively substantiate that our proposed modules and constructed network have made significant advancements in enhancing network accuracy and robustness against speckle variations.

Artificial intelligence-based speckle featurization and localization for ultrasound speckle tracking velocimetry

Deep learning-based super-resolution ultrasound (DL-SRU) framework has been successful in improving spatial resolution and measuring the velocity field information of a blood flows by localizing and tracking speckle signals of red blood cells (RBCs) without using any contrast agents. However, DL-SRU can localize only a small part of the speckle signals of blood flow owing to ambiguity problems encountered in the classification of blood flow signals from ultrasound B-mode images and the building up of suitable datasets required for training artificial neural networks, as well as the structural limitations of the neural network itself. An artificial intelligence-based speckle featurization and localization (AI-SFL) framework is proposed in this study. It includes a machine learning-based algorithm for classifying blood flow signals from ultrasound B-mode images, dimensionality reduction for featurizing speckle patterns of the classified blood flow signals by approximating them with quantitative values. A novel and robust neural network (ResSU-net) is trained using the online data generation (ODG) method and the extracted speckle features. The super-resolution performance of the proposed AI-SFL and ODG method is evaluated and compared with the results of previous U-net and conventional data augmentation methods under in silico conditions. The predicted locations of RBCs by the AI-SFL and DL-SRU for speckle patterns of blood flow are applied to a PTV algorithm to measure quantitative velocity fields of the flow. Finally, the feasibility of the proposed AI-SFL framework for measuring real blood flows is verified under in vivo conditions.

Research on the examination technology of connector pin skewing according to Blob analysis *

Pin skew detection is an important means to ensure the reliable operation of connectors. To address the issues of low accuracy and limited applicability in existing research, this paper proposes a connector pin skew detection method based on Blob analysis. Firstly, the image is segmented by incorporating the dimensional features of the tested connector to retain the effective information region in the image, reducing the computational workload for subsequent image processing. The image is preprocessed using an improved median filtering algorithm to effectively mitigate the interference of noise on the detection process. Secondly, a locally adaptive approach is employed to dynamically adjust the threshold, and morphological processing is applied to the pin image to enhance the pin speckle features. Subsequently, Blob analysis is utilized to analyze the connector pin speckles, obtaining data on the pin skew. Different evaluation criteria for pin skew data of various connectors are established to achieve quantitative assessment. Finally, experiments are conducted for pin skew detection of single-hole rectangular, double-hole rectangular, and single-hole circular connectors. The experimental results demonstrate that the proposed connector pin skew detection method can effectively detect various types of pin skew in connectors, with a detection accuracy better than 0.05 mm and a repeatability better than 0.03 mm. This method is suitable for automatic detection scenarios of connector pin skew.

Retinal OCT speckle as a biomarker for glaucoma diagnosis and staging.

This paper presents a novel image analysis strategy that increases the potential of macular Optical Coherence Tomography (OCT) by using speckle features as biomarkers in different stages of glaucoma. A large pool of features (480) were computed for a subset of macular OCT volumes of the Leuven eye study cohort. The dataset contained 258 subjects that were divided into four groups based on their glaucoma severity: Healthy (56), Mild (94), Moderate (48), and Severe (60). The OCT speckle features were categorized as statistical properties, statistical distributions, contrast, spatial gray-level dependence matrices, and frequency domain features. The averaged thicknesses of ten retinal layers were also collected. Kruskal-Wallis H test and multivariable regression models were used to infer the most significant features related to glaucoma severity classification and to the correlation with visual field mean deviation. Four features were selected as being the most relevant: the ganglion cell layer (GCL) and the inner plexiform layer (IPL) thicknesses, and two OCT speckle features, the data skewness computed on the retinal nerve fiber layer (RNFL) and the scale parameter (a) of the generalized gamma distribution fitted to the GCL data. Based on a significance level of 0.05, the regression models revealed that RNFL skewness exhibited the highest significance among the features considered for glaucoma severity staging (p-values of 8.6×10-6 for the logistic model and 2.8×10-7 for the linear model). Furthermore, it demonstrated a strong negative correlation with the visual field mean deviation (ρ=-0.64). The post hoc analysis revealed that, when distinguishing healthy controls from glaucoma subjects, GCL thickness is the most relevant feature (p-value of 8.7×10-5). Conversely, when comparing the Mild versus Moderate stages of glaucoma, RNFL skewness emerged as the only feature exhibiting statistical significance (p-value = 0.001). This work shows that macular OCT speckle contains information that is currently not used in clinical practice, and not only complements structural measurements (thickness) but also has a potential for glaucoma staging.

Accelerated Stress Testing of Perovskite Photovoltaic Modules: Differentiating Degradation Modes with Electroluminescence Imaging

Herein, electroluminescence (EL) and thermal imaging are used to examine p–i–n metal halide perovskite (MHP) photovoltaic (PV) mini‐modules (MA0.6FA0.4PbI3, 20 cells, 78 cm2) before and after indoor‐accelerated stress testing or outdoor deployment. Distinct spatial patterns in the EL images emerge, which depend on the external stress conditions experienced by the mini‐module. Imaging results highlight a distribution of dark speckle features that dominate after UV stress, attributed to widespread interfacial contact degradation. Lateral intensity gradients across cells dominate after thermal cycling (TC) stress, attributed to current crowding near scribe defects. While current–voltage analysis alone does not give full insight on the degradation process, this study shows that distinct degradation modes can be further defined by multimodal electro‐optical imaging (i.e., EL combined with photoluminescence and dark lock‐in thermography). Neither UV exposure nor TC‐accelerated stress testing alone replicates the same degradation signatures observed after outdoor deployment, suggesting that multiple degradation modes occur under concurrent stressors outdoors. Spatial characterization of degradation modes in MHP PV mini‐modules before and after accelerated stress testing lays the groundwork for developing targeted accelerated stress testing procedures through comparison with outdoor aging.

Effects of compressed speckle image on digital image correlation for vibration measurement

Digital image correlation (DIC) can accurately measure subpixel displacement field based on speckle images. However, in vibration measurement, the extensive high-resolution images captured by DIC systems impose significant data transmission and storage requirements on hardware. A framework combining image compression techniques with DIC methods is proposed. The framework effectively reduces the memory footprint of images while preserving the primary speckle features with subpixel errors. The DIC Challenge 2.0 image sets are employed in simulation to study the effects of compressing images on data storage and 2D DIC measurements, and the effectiveness are compared between JPEG and JPEG 2000 image compression techniques. A cantilever-beam is adopted to experimentally investigate the impacts of the compressed image sets on 3D surface deformation and vibration response measurement. The proposed framework is a valuable option for DIC vibration measurement with high efficiency, the JPEG lossy compression and JPEG 2000 lossless compression each has its own characteristics in performance of DIC based vibration measurement.

Built-Up Area Mapping for the Greater Bay Area in China from Spaceborne SAR Data Based on the PSDNet and Spatial Statistical Features

Built-up areas (BAs) information acquisition is essential to urban planning and sustainable development in the Greater Bay Area in China. In this paper, a pseudo-Siamese dense convolutional network, namely PSDNet, is proposed to automatically extract BAs from the spaceborne synthetic aperture radar (SAR) data in the Greater Bay Area, which considers the spatial statistical features and speckle features in SAR images. The local indicators of spatial association, including Moran’s, Geary’s, and Getis’ together with the speckle divergence feature, are calculated for the SAR data, which can indicate the potential BAs. The amplitude SAR images and the corresponding features are then regarded as the inputs for PSDNet. In this framework, a pseudo-Siamese network can independently learn the BAs discrimination ability from the SAR original amplitude image and the features. The DenseNet is adopted as the backbone network of each channel, which can improve the efficiency while extracting the deep features of the BAs. Moreover, it also has the ability to extract the BAs with multi-scale sizes by using a multi-scale decoder. The Sentinel-1 (S1) SAR data for the Greater Bay Area in China are used for the experimental validation. Our method of BA extraction can achieve above 90% accuracy, which is similar to the current urban extraction product, demonstrating that our method can achieve BA mapping for spaceborne SAR data.

Simultaneous Measurement of Temperature and Strain in Electronic Packages Using Multiframe Super-Resolution Infrared Thermography and Digital Image Correlation

Abstract For micro-electronic components and systems, reliability under thermomechanical stress is of critical importance. Experimental characterization of hotspots and temperature gradients, which can lead to deformation in the component, relies on accurate mapping of the surface temperature. One method of noninvasively acquiring this data is through infrared (IR) thermography. However, IR thermography is often limited by the typically low resolution of such cameras. Additionally, the unique surface finish preparations required to infer physical deformation using digital image correlation (DIC) generally interfere with the ability to measure the temperature with IR thermography, which prefers a uniform high emissivity. This work introduces a one-shot technique for the simultaneous measurement of surface temperature and deformation using multiframe super-resolution-enhanced IR imaging combined with DIC analysis. Multiframe super-resolution processing uses several subpixel shifted images, interpolating the image set to extract additional information and create a single higher-resolution image. Measurement of physical deformation is incorporated using a test sample with a black background and low-emissivity speckle features, heated in a manner that induces a nonuniform temperature field and stretched to induce physical deformation. Through processing and filtering, data from the black surface regions used for surface temperature mapping are separated from the speckle features used to track deformation with DIC. This method allows DIC to be performed on the IR images, yielding a deformation field consistent with the applied tensioning. While both the low- and super-resolution data sets can be successfully processed with DIC, super-resolution helps to reduce noise in the extracted deformation fields. As for temperature measurement, using super-resolution is shown to allow for better removal of the speckle features and reduce noise, as quantified by a lower mean deviation from the spatial moving average.

High-temperature digital image correlation techniques for full-field strain and crack length measurement on ceramics at 1200°C: Optimization of speckle pattern and uncertainty assessment

High-temperature mechanical tests coupled with Digital Image Correlation (DIC) on ceramics which exhibit rather low level of strain require to overcome extreme experimental conditions that usually can reduce significantly measurement accuracy. Thermal resistance of speckle pattern, black body radiation and heat haze are three main concerns, which should thus be taken into account while designing a high-temperature image acquisition setup. In this aim, an experimental procedure has been specifically designed in order to minimize the three above-mentioned disturbances. The main objective of this study is to select a suitable high-temperature resistant speckle pattern for mechanical characterization at 1200 °C (or above) on refractory ceramics. Most of tested speckle patterns were performed with white alumina adhesive and dark ceramic grains (silicon carbide or brown fused alumina). Different grain sizes of silicon carbide were tested. At first, different speckle patterns are compared in terms of DIC strain measurement uncertainty by discussing speckle features, some main DIC parameters and two image pre-treatments (low pass filter, image size reduction). Then, these speckle patterns are tested to analyse fracture behaviour of refractories through a Brazilian test. An enhanced digital image correlation technique (2P-DIC), dedicated to monitor the fracture behaviour, is applied to study the evolution of crack length. The best representation of crack progression has been achieved for sample surface covered with a fine SiC powder ranging from 50 to 100 µm. It is then possible to compare the fracture behaviour between 1200 °C and 20 °C and to show that the refractory exhibits more crack branching at 1200 °C in comparison with behaviour at room temperature.

Evaluation of crack opening phenomenon using subset-optimized digital image correlation

This paper presents crack opening phenomenon evaluation using digital image correlation (DIC) with a statistically optimized subset size. In conventional DIC analysis, the subset sizes varying from several pixels to more than hundred pixels have been often selected by experts' subjective judgement based on conventional subset size determination algorithms. Since these conventional subset size determination algorithms, however, calculate speckle pattern features at a certain location of a single target image, it is difficult to consider not only all speckle pattern features within region of interest (ROI) but also the random measurement noises during the digital image acquisition process. To overcome the technical limitation, a statistical optimization algorithm of the subset size, which calculates the optimal subset size by the 3-loop iteration of normalized cross correlation within the entire ROI, is newly proposed. In addition, the optimal subset-based DIC analysis is applied to crack opening phenomenon evaluation in a mock-up concrete specimen under step loading conditions. The validation test results show 3.6 μm maximum error compared with the ground truth which is obtained by direct measurement, while a conventional subset size determination algorithm-based DIC analysis produces the maximum error of 62.7 μm.

A Pilot Study on Convolutional Neural Networks for Motion Estimation From Ultrasound Images.

In recent years, deep learning (DL) has been successfully applied to the analysis and processing of ultrasound images. To date, most of this research has focused on segmentation and view recognition. This article benchmarks different convolutional neural network algorithms for motion estimation in ultrasound imaging. We evaluated and compared several networks derived from FlowNet2, one of the most efficient architectures in computer vision. The networks were tested with and without transfer learning, and the best configuration was compared against the particle imaging velocimetry method, a popular state-of-the-art block-matching algorithm. Rotations are known to be difficult to track from ultrasound images due to a significant speckle decorrelation. We thus focused on the images of rotating disks, which could be tracked through speckle features only. Our database consisted of synthetic and in vitro B-mode images after log compression and covered a large range of rotational speeds. One of the FlowNet2 subnetworks, FlowNet2SD, produced competitive results with a motion field error smaller than 1 pixel on real data after transfer learning based on the simulated data. These errors remain small for a large velocity range without the need for hyperparameter tuning, which indicates the high potential and adaptability of DL solutions to motion estimation in ultrasound imaging.

Ground Target Classification in Noisy SAR Images Using Convolutional Neural Networks

Speckle noise is an inherent but annoying property in the synthetic aperture radar (SAR) imaging. In this paper we investigate the influence of speckle on the classical convolutional neural network (CNN) for SAR target classification. Then a dual stage coupled CNN architecture, named despeckling and classification coupled CNNs (DCC-CNNs), is proposed to distinguish multiple categories of ground targets in SAR images with strong and varying speckle. It first applies the despeckling sub-network for noise reduction. After that, residual speckle features as well as target information would be learned by the classification sub-network in order to solve the noise robustness problem of CNN. Besides, a new quantitative measure is developed for the quality assessment of SAR target images. It takes into account structural properties of the speckled SAR image of the target of interest and consistency with visual perception. Finally, a series of comparative experiments and discussions are carried out to validate the proposed assessment criterion and DCC-CNNs. Using synthetic SAR images based on the public MSTAR datasets, results show that the overall classification accuracy for ten ground target classes could be higher than 82% at a variety of speckle noise levels.

Method to measure 3D deformation using defocused images of objects with artificial speckle features

A novel method to measure three-dimensional deformation based on defocused images of an object is presented in this paper. To do this, images of an object with artificial speckle features are taken by a camera. The in-plane coordinates of the points on a specimen surface are obtained according to the camera setup parameters and the defocused distance. The blur band width on the edges of the surface textures is used as a parameter to evaluate the blur degree of an image. Depth dimension is obtained by the relationship of the defocused distance and the blur degree. The relationship is given theoretically and experimentally. The digital image correlation method is used to trace each point on the specimen surface before and after deformation. Three-dimensional deformation can be obtained according to the displacement of each point. Reliability of the method is validated by two experiments.

SU‐E‐I‐90: Characterizing Small Animal Lung Properties Using Speckle Observed with An In‐Line X‐Ray Phase Contrast Benchtop System

Purpose:We demonstrate a novel X‐ray phase‐contrast (XPC) method for lung imaging representing a paradigm shift in the way small animal functional imaging is performed. In our method, information regarding airway microstructure that is encoded within speckle texture of a single XPC radiograph is decoded to produce 2D parametric images that will spatially resolve changes in lung properties such as microstructure sizes and air volumes. Such information cannot be derived from conventional lung radiography or any other 2D imaging modality. By computing these images at different points within a breathing cycle, dynamic functional imaging will be readily achieved without the need for tomography. Methods: XPC mouse lung radiographs acquired in situ with an in‐line X‐ray phase contrast benchtop system. The lung air volume is varied and controlled with a small animal ventilator. XPC radiographs will be acquired for various lung air volume levels representing different phases of the respiratory cycle. Similar data will be acquired of microsphere‐based lung phantoms containing hollow glass spheres with known distributions of diameters. Image texture analysis is applied to the data to investigate relationships between texture characteristics and airspace/microsphere physical properties.Results:Correlations between Fourier‐based texture descriptors (FBTDs) and regional lung air volume indicate that the texture features in 2D radiographs reveal information on 3D properties of the lungs. For example, we find for a 350 × 350 πm2 lung ROI a linear relationship between injected air volume and FBTD value with slope and intercept of 8.9×105 and 7.5, respectively.Conclusion:We demonstrate specific image texture measures related to lung speckle features are correlated with physical characteristics of refracting elements (i.e. lung air spaces). Furthermore, we present results indicating the feasibility of implementing the technique with a simple imaging system design, short exposures, and low dose which provides potential for widespread use in laboratory settings for in vivo studies.This research was supported in part by NSF Award CBET1263988.

A model for simulating speckle-pattern evolution based on close to reality procedures used in spectral-domain OCT

A robust model for simulating speckle-pattern evolution in optical coherence tomography (OCT) depending on the OCT system parameters and tissue deformation is reported. The model is based on the application of close to reality procedures used in spectral-domain OCT scanners. It naturally generates images reproducing properties of real images in spectral-domain OCT, including the pixelized structure and finite depth of unambiguous imaging, influence of the optical spectrum shape, dependence on the optical wave frequency and coherence length, influence of the tissue straining, etc. Good agreement with generally accepted speckle features and properties of real OCT images is demonstrated.

Imaging the electro-kinetic response of biological tissues with phase-resolved optical coherence tomography

AbstractIn this study, the electro-kinetic phenomena (EKP) induced in biological tissue by external electric field, while not directly visible in optical coherence tomography (OCT) images, were detected by analyzing their textural speckle features. During application of a low-frequency electric field to the tissue, speckle patterns changed their brightness and shape depending on the local tissue EKP. Since intensities of OCT image speckle patterns were analyzed and discussed in our previous publications, this work is mainly focused on OCT signal phase analysis. The algorithm for extracting local spatial phase variations from unwrapped phases is introduced. The detection of electrically induced optical changes manifest in OCT phase images shows promise for monitoring the fixed charge density changes within tissues through their electro-kinetic responses. This approach may help in the identification and characterization of morphology and function of healthy and pathologic tissues.