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This study presents a computational ghost imaging (GI) scheme that utilizes sequenced random speckle pattern illumination. The primary objective is to develop a speckle pattern/sequence that improves computational time without compromising image quality. To achieve this, we modulate the sequence of speckle sizes and design experiments based on three sequence rules for ordering the random speckle patterns. Through theoretical analysis and experimental validation, we demonstrate that our proposed scheme achieves a significantly better contrast-to-noise rate (CNR) compared to traditional GI at a similar resolution. Notably, the sequential GI method outperforms conventional approaches by providing over 10 times faster computational speed in certain speckle composition groups. Furthermore, we identify the corresponding speckle sizes that yield superior image quality, which are found to be geometrically proportional to the reference object area. This innovative approach utilizing sequenced random speckle patterns demonstrates potential suitability for imaging objects with complex or unknown shapes. The findings of this study hold great promise for advancing the field of computational GI and pseudo-thermal GI, addressing the need for improved computational efficiency while maintaining high-quality imaging.

Background Carotid intima-media thickness (IMT) measured in B-mode ultrasound image is an important indicator of Atherosclerosis disease. Speckle noise inherently present in ultrasounds’ thereby degrades the visual evaluation and limits the automated segmentation performance. The objective of this study is to investigate the effects of three despeckle filters on the segmentation of carotid IMT in ultrasound image. Methods Automated segmentation of IMT is achieved by utilizing fast fuzzy c-mean clustering and distance-regularized level set without re-initialization techniques. Manual segmentation has been done by an experienced radiologist. The performances of median, hybrid median and improved adaptive complex diffusion (IACDF) filters are examined and a quantitative and qualitative comparison among these filters has been reported on 151 DICOM images. Bland–Altman plots were used to compare IMT results of these filters. Furthermore, performances of above three filters are evaluated under different noise levels by individually adding speckle and salt and pepper noise in ten randomly selected images from 151 DICOM dataset. Plots between noise and quality evaluation metric parameters are used to compare de-noising performance of these filters. Results The average processing time per image of proposed IMT measurement technique without-filter and with filter is approx 15.39 s max. Conclusion It is shown that the median filter (window 5 × 5) measures better than hybrid median and IACDF filters. Finally, concluded that de-noising of ultrasound image before segmentation procedure certainly improves segmentation accuracy. Furthermore, it is observed that these filters do not impose serious computational burden and entail moderate processing time.

As an increasingly recognized optical interferometric technique for nondestructive testing and evaluation, shearography has attracted extensive interest in various industrial applications. However, it suffers from the ignorance of the whole process of dynamic surface deformation and the difficulty in determining the depth-resolved information of inhomogeneities. In this regard, a novel speckle pattern interferometric modality named photothermal radar shearography is proposed. Unlike the differential mode of conventional shearography, the present work focuses on the dynamic surface displacement field channel technique with more comprehensive understanding of the subsurface structural information. With the utilization of frequency modulated photothermal excitation, the depth-distributed information of subsurface structures and inhomogeneities is encoded into the induced dynamic surface deformation. Using Hilbert transform and least-square method solved by discrete cosine transform, the time-domain interference signal of each pixel from the recorded speckle patterns sequence is demodulated and unwrapped to obtain the transient full-field shearographic phase distribution which indicates the dynamic surface deformation. In the meanwhile, incrementally delayed cross-correlation matched filtering allows for the localization of axial energy distribution to generate depth-selective structural display for tomography. This proposed transient-based interferometric methodology thus enables depth-tomographic profiles and three-dimensional visualization of subsurface anomalies for the first time, which significantly improves the superiority and attractiveness of shearography in providing insight into the status, performance and reliability of industry.

The speckle noise is the major area of concern in ultrasound imaging. Speckle degrades the speed and accuracy of subsequent image processing tasks such as segmentation, description etc. But reduction of speckle may cause blurring or loss of edges and important features. In our work, we have employed a novel thresholding rule based on wavelet transform for speckle reduction from ultrasound images. The wavelet transform performs multi-scale analysis of the given image by treating different frequency components present in an image separately. The experiment results exhibit that the proposed thresholding rule gives better results for speckle reduction, edge preservation and feature preservation for medical ultrasound images, as compared with the existing thesholding rules.

In this paper, we propose optimum and near-optimum adaptive coherent detectors of radar targets in compound-Gaussian clutter with generalized inverse Gaussian texture (CG-GIG clutter). The target amplitude and the speckle covariance matrix are modeled as unknown quantities to be estimated. On the basis of the two-step generalized likelihood ratio test (GLRT) and the estimate of the speckle covariance matrix, the optimum coherent detector and its adaptive version are designed in this paper. It is demonstrated that the proposed optimum coherent detector contains three common detectors, which are the optimum K detector (OKD), the generalized likelihood ratio test linear-threshold detector (GLRT-LTD), and the generalized likelihood ratio test detector for compound-Gaussian clutter with inverse Gaussian texture (GLRT-IG). Due to the existence of the modified Bessel function depending on data, the optimum coherent detector is computationally unrealizable. Therefore, a near-optimum coherent detector with a new structure also named match filter (-MF) detector and its adaptive version (-AMF) are proposed, which decreases the computational cost. The proposed near-optimum coherent detector contains two common detectors, the GLRT-LTD and the -MF detector in K-distributed clutter, and has a comparable detection performance of the near-optimum detector in CG-IG clutter which was proposed before. Theoretical analysis and numerical experiments illustrate that the proposed two detectors for CG-GIG clutter have the constant false alarm ratio (CFAR) property relative to the estimate of the speckle covariance matrix and Doppler steering vector. Moreover, the detection performance of the two coherent detectors are evaluated by the simulated and real data.

Imaging applications tailored towards ultrasound-based treatment, such as high intensity focused ultrasound (FUS), where higher power ultrasound generates a radiation force for ultrasound elasticity imaging or therapeutics/theranostics, are affected by interference from FUS. The artifact becomes more pronounced with intensity and power. To overcome this limitation, we propose FUS-net, a method that incorporates a CNN-based U-net autoencoder trained end-to-end on 'clean' and 'corrupted' RF data in Tensorflow 2.3 for FUS artifact removal. The network learns the representation of RF data and FUS artifacts in latent space so that the output of corrupted RF input is clean RF data. We find that FUS-net perform 15% better than stacked autoencoders (SAE) on evaluated test datasets. B-mode images beamformed from FUS-net RF shows superior speckle quality and better contrast-to-noise (CNR) than both notch-filtered and adaptive least means squares filtered RF data. Furthermore, FUS-net filtered images had lower errors and higher similarity to clean images collected from unseen scans at all pressure levels. Lastly, FUS-net RF can be used with existing cross-correlation speckle-tracking algorithms to generate displacement maps. FUS-net currently outperforms conventional filtering and SAEs for removing high pressure FUS interference from RF data, and hence may be applicable to all FUS-based imaging and therapeutic methods.

Objective: To assess validity of Laser Speckle Contrast Imaging (LSCI) for the measurement of burn wound healing potential (HP) in a burn centre patient population, based on Laser Doppler Imaging (LDI) as reference standard. Method: A single-centre prospective observational cohort study was performed between September–December 2019. A total of 50 burns in 14 patients were included. Imaging and data collection were standardized. Correlation between LSCI and LDI flux values was tested. ROC curves were developed to test the discriminative ability of LSCI between LDI HP categories. Results: Number of adults and children were equal. Mean total body surface area burnt was 5.5%. Main burn causes were scalds (64%) and flame burns (36%). LSCI set-up and imaging duration was 3 min and 10 s, respectively. LSCI was able to discriminate between wounds with early versus delayed HP (21 days, similar sensitivity (74%) and specificity (95%) were found (p < 0.001). Correlations between LSCI and LDI flux values were moderate (21 days). Conclusion: LSCI shows good validity for the prediction of burn wound HP. It is a highly feasible, patient and physician friendly tool.

Interstitial fibrosis, common to most chronic kidney diseases, can potentially affect the speckle patterns of kidney ultrasound (US). Here we use Radiomics features derived from US images to identify kidneys with chronic kidney disease.B-mode US without speckle reduction was performed on a cohort of CKD patients (n = 75) and healthy subjects (n = 27). Images of the patients with renal cysts, agenesis and calculi were excluded. After background subtraction, regions of interest were selected from each kidney. Four hundred and sixty-five Radiomics features including first and second-order gray level statistics were calculated on the selected regions. Second-order features were also calculated on wavelet transformed images. A random forest model was used to identify the most important features that can differentiate healthy and diseased kidneys. The ten most important features, based on the Gini index, were used to train a support vector machine. Synthetic minority oversampling technique was used to remove over fitting.Wavelet transformed, Gray Level Run Length Matrix based Normalized Run Length Non-uniformity, WT (LH) (GRLN) was identified as the most significant feature in differentiating CKD and healthy kidneys (accuracy - 0.85, sensitivity - 1.0). The mean WT (LH) GRLN of healthy kidneys (0.40 ± 0.01) was significantly higher (p0.01) than that of the CKD kidneys (0.24 ± 0.01). According to the Gini Index, the differentiability of WT (LH) GRLN was highest when the long axis of the kidney was oriented perpendicular to the columns of the image matrix.Radiomics features based on wavelet transformation are sensitive to directionality of US speckle patters and can be successfully used to differentiate CKD and healthy US kidney images.

In this paper, a novel method for simultaneous measurement of rotation speed and vibration based on self-mixing speckle interference (SMSI) has been demonstrated. In view of the autocorrelation characteristic of SMSI, the time delay of laser incident during one rotation period is determined, and the rotation speed of the object is calculated according to the mathematical relationship. At the same time, the signal square-wave conversion and the global maximum fringe-counting method are proposed to measure the vibration displacement subject to the rotation. The theory and signal processing means are introduced in detail, and a series of experiments employing computer disk at different positions with various rotation speeds indicate that the proposed method achieves a simple, efficient, and accurate multi-parameter measurement.

Efficient fabrication and high reliability of flexible integrated circuit package substrates (FICS) urgently need effective defect detection methods. Based on the defect categories of FICS images, including yellow circuit backgrounds, film backgrounds with speckles, and black backgrounds, a novel rapid detection algorithm of impurity defects in FICS images is proposed. Three successive procedures are indispensable to realize the rapid detection with high accuracy, including image contrast enhancement based on image gray value analysis, obtaining the standard templates to weaken the texture interferences, and the probability calculation of the background feature of each pixel in the edge region between background and defect. Then, the accurate identification of film/black backgrounds and impurity defects in high-density FICS images is achieved. Furthermore, the comparisons of time–accuracy tradeoff obtained by using existing methods (Lk-means, Ostu, GMEM, SPCNN, MBGCT, FTC) and the proposed algorithm are carried out to experimentally verify the feasibility. The defect detection algorithm will facilitate a solution for the high similarity between the inherent texture features of FICS and external impurity defects at micrometer scale and substantially improve the detection accuracy and efficiency of high-density FICS, which will meet the real-time industrial requirement for the rapid fabrication of high-density FICS.

BACKGROUND: Hemophilia is an X-linked inherited disorder primarily affecting males, its major types are type A (deficiency in factor VIII) and B (deficiency in factor IX), and is considered to be the most common severe congenital coagulation factor deficiency. The present study was designed to test whether any differences in left ventricular (LV) rotational mechanics could be demonstrated between male patients with hemophilia and healthy controls using three-dimensional speckle-tracking echocardiography (3DSTE)-derived virtual LV models. METHODS: The present study consisted of 17 patients with hemophilia, however, 3 patients were excluded due to insufficient image quality. In the remaining patient population, 12 patients had hemophilia A and 2 patients had hemophilia B (mean age: 42.2±18.9 years, all males). The control group comprised 16 age-matched healthy subjects (46.0±5.9 years, all males). RESULTS: None of the routine two-dimensional echocardiographic data differ between patients with hemophilia and controls. None of the patients and controls showed ≥ grade 1 valvular regurgitations and had valvular stenoses. In one subject, the near absence of LV twist called as LV “rigid body rotation” could be detected, data of which were managed separately. While 3DSTE-derived apical LV rotation was 3.65 degrees, basal LV rotation proved to be 3.57 degrees leading to 0.08-degree LV apico-basal gradient suggesting counterclockwise LV “rigid body rotation”. In the remaining patients, both LV apical rotation (7.25±6.20 vs. 10.39±4.16 degrees, P

A polarimetric synthetic aperture radar (PolSAR) sensor is able to collect images in different polarization states, making it a rich source of information for target characterization. PolSAR images are inherently affected by speckle. Therefore, before deriving ad hoc products from the data, the polarimetric covariance matrix needs to be estimated by reducing speckle. In recent years, deep learning-based despeckling methods have started to evolve from single-channel SAR images to PolSAR images. To this aim, deep learning-based approaches separate the real and imaginary components of the complex-valued covariance matrix and use them as independent channels in standard convolutional neural networks (CNNs). However, this approach neglects the mathematical relationship that exists between the real and imaginary components, resulting in suboptimal output. Here, we propose a multistream complex-valued fully convolutional network (FCN) (CV-deSpeckNet1) to reduce speckle and effectively estimate the PolSAR covariance matrix. To evaluate the performance of CV-deSpeckNet, we used Sentinel-1 dual polarimetric SAR images to compare against its real-valued counterpart that separates the real and imaginary parts of the complex covariance matrix. CV-deSpeckNet was also compared against the state of the art PolSAR despeckling methods. The results show that CV-deSpeckNet was able to be trained with a fewer number of samples, has a higher generalization capability, and resulted in higher accuracy than its real-valued counterpart and state-of-the-art PolSAR despeckling methods. These results showcase the potential of complex-valued deep learning for PolSAR despeckling.

Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle noise, which results in the deterioration of image quality, so the removal of speckle in SAR image is very important for the subsequent high-level image processing. In order to balance the relationship between denoising and texture preservation, we propose a multiscale residual dense dual attention network (MRDDANet) for SAR image denoising. This algorithm can effectively suppress the speckle while fully retaining the texture details of the image. In MRDDANet, shallow features are extracted from the noisy images by multiscale modules with different kernel sizes, and then, the extracted shallow features are mapped to the residual dense dual-attention network to obtain the deep features of SAR image. Finally, the final denoising image is generated through global residual learning. MRDDANet has advantages of both multiscale blocks and residual dense dual attention networks. The dense connection can fully extract features in the image, and the dual-channel attention enables MRDDANet to pay more attention to noise information, which is beneficial to remove noise and keep the details of the original image at the same time. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only improves various objective indicators but also shows great advantages in visual effects.

Electronic Speckle Pattern Interference (ESPI) is a non-contact optical technique generally used for the measurement of surface deformations of various kinds. With the help of phase retrieval algorithms, phase of the deformed object is calculated in this method which can be then converted into displacement information. This paper demonstrates the use of ESPI method to measure the thickness of some nano-sized metallic thin films. Calculated thickness values are in good agreement with the values measured via Profilometer. The least thickness (z-axis) measured by this method is ~ 52 ± 3.1 nm. The z-axis resolution of the used method is at par with other expensive microscopy techniques like atomic force microscopy. Therefore, a low-cost alternative to expensive microscopy techniques is presented in this paper as far as z-axis is concern. Surface roughness of thin films can also be calculated using retrieved surface profiles and they are also found to be in the nanometer range.

Contains fulltext : 248292.pdf (Publisher’s version ) (Open Access) BACKGROUND: Skin microvasculature changes are crucial in psoriasis development and correlate with perfusion. The noninvasive Handheld Perfusion Imager (HAPI) examines microvascular skin perfusion in large body areas using laser speckle contrast imaging (LSCI). OBJECTIVES: To (i) assess whether increased perilesional perfusion and perfusion inhomogeneity are predictors for expansion of psoriasis lesions and (ii) assess feasibility of the HAPI system in a mounted modality. METHODS: In this interventional pilot study in adults with unstable plaque psoriasis, HAPI measurements and color photographs were performed for lesions present on one body region at week 0, 2, 4, 6 and 8. The presence of increased perilesional perfusion and perfusion inhomogeneity was determined. Clinical outcome was categorized as increased, stable or decreased lesion surface between visits. Patient feedback was collected on a 10-point scale. RESULTS: In total, 110 lesions with a median follow-up of 6 (IQR 6.0) weeks were assessed in 6 patients with unstable plaque psoriasis. Perfusion data was matched to 281 clinical outcomes after two weeks. A mixed multinomial logistic regression model revealed a predictive value of perilesional increased perfusion (OR 9.90; p

Speckle filtering is an important step for target detection in SAR images since this effect makes it difficult or even impossible to extract information from these images. There are several filters available in the literature although evaluating their performances is not a trivial task since it requires comparing the filtered images with a speckle-free image, which is generally unknown. This evaluation is even more complex when the features in the images are heterogeneous, for example, from tropical forests. The objective of this study is to evaluate the performance of the Lee, deGrandi, GammaMAP, single Anisotropic Nonlinear Diffusion (ANLD), multitemporal ANLD, Fast Adaptive Nonlocal SAR (FANS), and Fast GPU-Based Enhanced Wiener filters to reduce the speckle present in the COSMO-SkyMed Stripmap X-band images from the Brazilian Amazon forest region. The evaluation was conducted qualitatively through the visual inspection of the ratio image and the edge detection in the ratio images and quantitatively through the αβ estimator and other statistical parameters of the filtered images. The GammaMAP filter showed the best performances, both qualitatively and quantitatively, and the FANS filter only qualitative.

Parameter estimation is a problem of interest when designing new remote sensing instruments, and the corresponding lower performance bounds are a key tool to assess the performance of new estimators. In global navigation satellite systems reflectometry (GNSS-R), a noncoherent averaging is applied to reduce speckle and thermal noise, and subsequently the parameters of interest are estimated from the resulting waveform. This approach has been long regarded as suboptimal with respect to the optimal coherent one, which is true in terms of detection capabilities, but no analysis exists on the corresponding parameter estimation performance exploiting GNSS signals. First, we show that for certain signal models, both coherent and noncoherent Cramer-Rao bounds are equivalent, and therefore, any maximum likelihood estimation coherent/noncoherent combination scheme is efficient (optimal) at high signal-to-noise ratios. This is validated for an illustrative GNSS-R estimation problem. In addition, it is shown that considering the joint delay/Doppler/phase estimation problem, the noncoherent performance for the delay is still optimal, which is of practical importance for instance in altimetry applications.

Image denoising is considered an effective initial processing step in different imaging applications. Over the years, numerous studies have been performed in filtering for different kinds of noises. The block matching with 3-D group filtering has added a new dimension and better results for denoising techniques. This work aims to establish a novel denoising method for multiplicative (speckle) noise employing 3-D arrays resulted from gathering similar patches in clustered areas of an image through the sparse representation based on discrete wavelet transform (DWT) and maximum a posteriori (MAP) estimator technique. Experimental results justified a good quality of the filtered image by the novel framework, which appears to demonstrate better denoising performance against state-of-the-art algorithms according to the objective criteria [peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and edge preservation index (EPI)] values and subjective visual perception.

This article addresses the detection problem of radar targets embedded in nonhomogeneous and non-Gaussian sea clutter. Nonhomogeneity leads to insufficiency of secondary data for estimating the clutter speckle covariance matrix, and non-Gaussianity causes sea clutter to become spiky. In this article, the persymmetry of the clutter covariance matrix is adopted to alleviate the requirement of secondary data, and the prior distribution of clutter texture is exploited to tackle the clutter non-Gaussianity. Based on such clutter knowledge, three adaptive detectors are proposed according to the principles of the generalized likelihood ratio test, the Wald test, and the Rao test. It is proven that three detectors ensure constant false alarm rate (CFAR) properties with respect to both the clutter speckle covariance matrix and the clutter power mean. Simulation experiments show that three detectors outperform their competitors.

Ultrasound is an essential tool for diagnosing and monitoring diseases, but it can be limited by poor image quality. Lag-one coherence (LOC) is an image quality metric that can be related to signal to noise ratio (SNR) and contrast to noise ratio (CNR). In this study, we examine matched LOC and B-mode images of the liver to discern patterns of low image quality, as indicated by lower LOC values, occurring beneath the abdominal wall, near out-of-plane vessels, and adjacent to hyperechoic targets such the liver capsule. These regions of suppressed coherence are often occult; they present as temporally stable uniform speckle on B-mode images, but the LOC measurements in these regions suggest substantially degraded image quality. Quantitative characterization of the coherence suppression beneath the abdominal wall shows a consistent pattern both in simulations and in vivo; sharp drops in coherence occurring beneath the abdominal wall asymptotically recover to a stable coherence at depth. Simulation studies suggest that abdominal wall reverberation clutter contributes to the initial drop in coherence but does not influence the asymptotic LOC value. Clinical implications are considered for contrast loss in B-mode imaging and estimation errors for elastography and Doppler imaging.

In this communication, we present a novel method to measure local optical dichroism (OD) in opaque crystal powder suspensions using photoacoustic (PA) effect. Our method is based upon the novel laser speckle contrast technique, in combination with a simple statistical approach, we were able to measure the OD of chiral crystals suspensions under completely random orientation.

Deep learning-based synthetic aperture radar (SAR) image change detection has recently achieved remarkable success due to its great potential for extracting abstract features. However, the existing methods still have room for improvement in dealing with the speckle of SAR images. In this letter, a deep spatial-temporal gray-level co-occurrence aware convolutional neural network (STGCNet) is proposed, which can effectively mine the spatial-temporal information of the bitemporal images and obtain the speckle-robust results by introducing the 3-D gray-level co-occurrence matrix (3-D-GLCM) as auxiliary feature. Specifically, representative features are extracted from original image pairs and their corresponding 3-D-GLCM through two-stream network, followed by an adaptive fusion module to balance the contribution of each branch. Then, the final binary change detection results are obtained by a fully connected layer. The training process relies on reliable labels generated by unsupervised models rather than manually annotated data, and therefore, the proposed STGCNet is practical in reality. Experiments on synthesized and real SAR data sets demonstrate the robustness and competitiveness of the proposed method compared with the state-of-the-art algorithms.

This study has been focused in the introduction of the theoretical and practical foundations of the Digital Image Correlation DIC technique to undergraduate students at EPN through the implementation of a Laboratory DIC practice. In the experimental test, an elastomer with different hole shapes is used as a test specimen, which should have a pattern of random speckles/dots in its surface, where the load applied is sequential or staggered to obtain images. The project evaluates three specialized image correlation packages: Digital Image Correlation Engine (DICe), Matlab's open source 2D Digital Image Correlation software Ncorr, and finally the GOM correlate software. The stress concentrator values result around different hole shapes are compared with analytically solutions of theory of elasticity and other formulas found in literature. Finally, a numerical simulation is carried out on a commercial FEA software to further compare experimental dossier. Data suggest a good agreement between procedures of analysis that allow the correct introduction of theoretical and experimental foundations of the Digital Image Correlation DIC among EPN community.

The speckle interference seriously degrades the quality of synthetic aperture radar (SAR) image. The existing despeckling algorithms still struggle to remove noise and preserve details simultaneously. In order to enhance the noise suppression and detail restoration performance, this article specially presents a spatial and transform domain convolutional neural network (STD-CNN) model, which yields an integrated feature representation and learning framework for despeckling. In addition, an innovative feature refinement strategy is proposed to further reduce the detail loss by isolating detail features from noise features. Extensive experiments on synthetic and real SAR images demonstrate that the proposed method outperforms the existing SAR despeckling methods on both quantitative and qualitative assessments. With partial modification, the STD-CNN model can still be extended to other image restoration tasks.

The advent of deep learning has made a significant advance in ship detection in synthetic aperture radar (SAR) images. However, it is still challenging since the amount of labeled SAR samples for training is not sufficient. Moreover, SAR images are corrupted by speckle noise, making them complex and difficult to interpret even by human experts. In this letter, we propose a novel SAR ship detection framework that leverages label-rich electro-optical (EO) images for more plentiful feature representations, and delicately addresses the speckle noise in SAR images. To this end, we first introduce a multistage domain alignment module that reduces the distribution discrepancies between EO and SAR feature maps at local, global, and instance levels. This allows enriching SAR representations by gradually instilling cross-domain knowledge from a large-scale EO image dataset. We further design a blind-spot layer for feature extraction to suppress the influence of speckles. Experimental results on the high-resolution SAR images dataset (HRSID) show that our detection performance achieves average precision (AP) 5.5% better than the current state-of-the-arts that exploits SAR images only. Our method significantly improves the detection performance with higher speckle noises, demonstrating stronger robustness than the conventional methods.

The preparatory operations of DIC (Digital Image Correlation) tests were investigated in this study, with special emphasis on specimen cleaning and painting operations. As it is well known, DIC tests are non-contact and applied in materials research, the analysis of complex structures, and, nowadays, the construction industry. The use of DIC technologies has seen a dynamic increase in all scientific fields. In our study, aluminum body panels for automotive and railway applications were tested using this technique. There are many articles on proper patterning in the literature but fewer on preparation and priming. These are critical for a successful DIC measurement. This paper looks at different surface cleaners and primers with different grading procedures and will also determine the time window within which the paint should be applied. Finally, the GOM ARAMIS system was applied to measure and characterize the painted surface and visible deformation defects resulting from inadequate painting.

By simulating the dependence of laser directionality and the possibility of multimode lasing on the sidewall angle of Aquarius-shaped cavity, we achieved the optimized parameters for low-coherence and high-directivity laser output. The measured divergence angle, the speckle contrast, and the output power are 7.2°, 4.53 %, 700 mW@4 A, respectively, in the experiment. Then we introduced patterned electrode on the P-side of the laser, and obtained adjustable coherence with the switched speckle contrast between 4.8 % and 27.8 %. This highly directional and adjustable coherence on-chip complex cavity laser prospectively offers a new choice for biological imaging.

Morphology measurement is essential for a better understanding of irregular particle dynamics. Digital holography of side scattering is applied to measure the morphology and 3D position of pulverized coal particles. The digital holographic recording of particle side scattering is modelled and the formula is verified by experiments. A side scattering holography system is established to record the hologram of pulverized coal particles. The hologram of particles with several scatterers has uneven chirp fringes which is mainly influenced by scatterer shape, while the hologram of particles with multiple scatterers is a superposition of single scatterer fringes with a speckle background which is influenced by both scatterer shape and location. The Moire pattern which appears easily in digital holography of side scattering is also analyzed. The proposed technique is validated by in-line holography and is proven to have an advantage in the identification of irregular particle morphological and surface features simultaneously.

Evaluating the filtering process of the digital speckle pattern interferometry (DSPI) of a single image by the traditional methods can result in under- or over-filtering. To solve this problem, this paper proposes an image-filtering evaluation method based on the Sobel operator and image entropy. First, the Sobel operator is introduced to convolute a filtered image to obtain a gradient image of a DSPI. Then, by analyzing the gradient image, image entropy is calculated according to the characteristics of the gradient map. Finally, the proposed method is verified by experiments. The experimental results show that the proposed method can quantitatively evaluate the filtering process of the DSPI.

The production of parts and (or) finished products in electronics, mechanical engineering and other industries is inextricably linked with the control of the accuracy and cleanliness of the processed surfaces. Currently existing meters of parameters of statistically uneven surfaces, both contact and non-contact have some disadvantages, as well as limitations due to methods and design features of measurement. Speckle interferometric methods for measuring parameters of statistically uneven surfaces make it possible to get away from some disadvantages inherent in existing methods and measurements. The use of methods of statistical radio engineering, methods of optimization of statistical solutions and estimates of parameters of predictive distributions for optimal radio engineering system synthesis is promising for the analysis and processing optical-electronic coherent laser space-time signals (speckle images) form with the laser radiation scattered by statistically uneven surfaces. This work synthesizes the optimal algorithm and structure for analyzing the parameters of statistically-temporal surfaces based on spatio-temporal processing of optical speckle interference signals and images using modern methods of optimal synthesis of radio engineering and coherent optoelectronic systems. In this work, an algorithm for processing optical signals scattered by statistically uneven surfaces is synthesized and investigated for problems of optimal estimation of parameters and statistical characteristics of statistically uneven surfaces. A block diagram of the optical contactless device for evaluating the parameters of statistically uneven surfaces is proposed. The limiting errors of estimation parameters of statistically uneven surfaces and the optimal installation angles of the emitters and the optical receiver are investigated. Equations are obtained for estimating the root-mean-square height of the ridges and the correlation radius of small-scale statistically uneven surfaces in the approximation of small perturbations. The proposed method for evaluating the parameters of statistically uneven surfaces allows to increase the accuracy of measurements, to conduct a non-contact assessment of the parameters - even statistically uneven surfaces that have geometric surface irregularities or located in hard-to-reach places, for example, grooves, holes, as well as products of cylindrical, spherical and other shapes.

Objectives The purpose of this study was to use speckle tracking analysis to evaluate the size, shape, and function of the atrial chambers in normal fetuses and develop a z-score calculator that can be used in future studies in fetuses at risk for cardiovascular disease. Methods The control group consisted of 200 normal fetuses examined between 20 and 40 weeks of gestation in which speckle tracking analysis of right (RA) and left (LA) atrial chambers was performed. The atrial end-diastolic and end-systolic endocardial borders for each chamber were identified from which measurements of atrial length, width, area, and volume were computed. Equations were derived using fractional polynomial regression analysis to compute z-score equations. Results The LA end-diastolic volume, RA and LA end-diastolic area, length, base width, and mid-chamber widths increased with gestational age and fetal size. Left atrial emptying and ejection volumes increased with gestational age and fetal size. The fractional area change was significantly less for the RA than the LA. The LA base and mid-chamber fractional shortening were significantly greater than the RA. There was a significant difference between the RA and LA global contractile strain. Conclusion Mean and standard deviation equations for each of the measurements described in this study were computed to create a z-score calculator that can be utilized in the clinical environment when evaluating fetuses with suspected atrial pathology that could alter the size, shape, and function of the atrial chambers.

Purpose The purpose of this study was expected to simultaneously monitor the surface roughness of each solidified layer, the surface roughness of the metal powder, the outline of the solidified layer, and the height difference between the solidified layer and the metal powder. Design/methodology/approach In the proposed approach, color images with red, green and blue fringes are used to measure the shape of the built object using a three-step phase-shift algorithm and phase-unwrapping method. In addition, the surface roughness is extracted from the speckle information in the captured image using a predetermined autocorrelation function. Findings The feasibility and accuracy of the proposed system were validated by comparing it with a commercial system for an identical set of samples fabricated by a selective laser melting process. The maximum and minimum errors between the two systems are approximately 24% and 0.8%, respectively. Originality/value In the additive manufacturing field, the authors are the first to use fringe detection technology to simultaneously measure the profile of the printed layer and its surface roughness.

Problems. Computer processing of speckle interferograms for optical diagnostics of biological micro-objects makes it possible to determine the optical and physical parameters of the components of a biological object. An important characteristic of the developed model is its versatility in its application, which makes it possible to provide the necessary adequacy and uniqueness of optical relationships with the original. During computer processing, the distribution of radiation in the environment of temperature fields is calculated. Purpose. The purpose of modelling is to study the interactions of laser radiation with biological micro-objects to improve and increase the efficiency of laser diagnostics. Implementation technique. For study the interaction of electromagnetic radiation with human erythrocytes, the speckle interferometry method was used, which was taken as the basis for constructing a laboratory bench. The developed laboratory bench made it possible to obtain speckle interferograms for four pathological samples of human erythrocytes. To process speckle interferograms of samples, a computer model was created in the Matlab environment, which allows examining images in JPEG format with a resolution of 72x72 dots per inch. Research results. As a result of computer processing and modelling, the surfaces of the normalized cross-correlation between the two investigated speckle-interferogram samples are calculated. The obtained dependences showed that the largest peak of the correlation coefficient falls on the central part of speckle interferograms of blood samples. The average radiation intensity for the samples does not diverge significantly, but it is shown that the radiation intensity of one of the samples is 12% higher than the radiation intensity for the reference sample. The results presented make it possible to determine the pathology in blood samples based on the study of speckle interferograms in the optical diagnosis of biological objects. Conclusions. The method for determining pathology in human blood samples by studying speckle interferograms of biological objects and computer processing of optical images of samples based on a model in the Matlab environment is proposed. Key words: speckle-interferograms, erythrocytes of blood, diagnostics of biological micro-objects, image processing, model, Matlab

The study focused on the application of speckle tracking algorithm in the segmentation of cardiac color ultrasound images of patients with atrial fibrillation combined with heart failure. First, the optical flow method and block matching method were introduced on the basis of multiphase level set algorithm. Then, the pyramid block matching method was applied to build a pyramid model from bottom to top according to each image, and thus a new segmentation algorithm of cardiac color ultrasound image was constructed. The speckle tracking algorithm based on the pyramid block matching method was applied to segment cardiac color ultrasound images of 136 patients with atrial fibrillation and heart failure and compared with the traditional diagnosis for the sensitivity, specificity, and accuracy. It was found that the curve smoothness and accuracy of the algorithm in this study were better than the traditional level set algorithm, and it made up for the shortcomings of the traditional method. The proportion of patients of class III-IV cardiac function was significantly higher than that of non-atrial fibrillation patients, and the difference was statistically significant ( P < 0.05 ); patients of classes III-IV showed better left ventricular ejection fraction (LVEF) (42.4 ± 2.8%), left ventricular end-diastolic diameter (LVED) (58.7 ± 7.4 mm), left ventricular end-systolic diameter (LVSD) (49.3 ± 5.6 mm), and left atrial inner diameter (LAD) (55.0 ± 1.4 mm) versus those of classes I-II, of whom the corresponding indexes were 58.8 ± 3.3%, 48.5 ± 5.9 mm, 33.5 ± 4.5 mm, and 45.2 ± 2.0 mm. The accuracy of diagnosis based on the algorithm of this study (93.22%) was significantly higher than that of traditional method (79.23%), and the differences were statistically significant ( P < 0.05 ). In conclusion, the algorithm in this study improves the segmentation accuracy and smoothness of the curve, which is suggested in clinic.

To expand the measurement target from that containing simple cylindrical-shaped components to complex-shaped components that have several directional tilted surfaces, we determined an issue in measuring the rough surfaces with laser distance measurement. The issue is that laser speckle causes measurement error, which amount, we found, can be determined by the diameter and irradiation angle of the measurement laser beam. We then developed two error-reduction techniques that are based on the optical properties of a detected waveform and power. We conducted a basic experiment and a feasibility study with actual samples, to evaluate the distance measurement performance of targets with various surface roughness and irradiation angles. The roughness and angle range of the evaluation was determined considering actual measurement needs in parts manufacturing. We confirmed that the measurement error, whose maximum value was nearly 80 μm, decreased to less than 20 μm using our proposed local-maximum-power-based technique.

Right ventricular (RV) strain measurements from ultrasound via speckle-tracking techniques are being used more frequently as a non-invasive diagnostic tool for a variety of cardiopulmonary pathologies. However, despite the clinical utility of ultrasound RV strain measurements, quantification of RV strain in rodents remains difficult owing to unique image artifacts and non-standardized methodologies. We demonstrate here a simple approach for measuring RV strain in both mice and rats using high-frequency ultrasound and automated speckle tracking. Our results show estimated peak RV free-wall longitudinal strain values (mean ± standard error of the mean) in mice (n = 15) and rats (n = 5) of, respectively, −10.38% ± 0.4% and −4.85% ± 0.42%. We further estimated the 2-D Green–Lagrange strain within the RV free wall, with longitudinal components estimated at −5.7% ± 0.48% in mice and −2.1% ± 0.28% in rats. These methods and data may provide a foundation for future work aimed at evaluating murine RV strain levels in different disease models.

Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used in applications, such as change detection, image restoration, segmentation, detection, and classification. With reference to the synthetic aperture radar (SAR) domain, the application of DL techniques is not straightforward due to the nontrivial interpretation of SAR images, especially caused by the presence of speckle. Several DL solutions for SAR despeckling have been proposed in the last few years. Most of these solutions focus on the definition of different network architectures with similar cost functions, not involving SAR image properties. In this article, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed. This is achieved by the definition of a peculiar loss function obtained by the weighted combination of three different terms. Each of these terms is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties, and strong scatterers identification. Their combination allows balancing these effects. Moreover, a specifically designed architecture is proposed to effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared with the state-of-art despeckling algorithms, both from a quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous, and extremely heterogeneous.

Synthetic aperture radar (SAR) image segmentation is a challenge due to its inherent speckle. Gamma distribution is believed to be an appropriate statistical model to describe the characteristics of speckle in SAR images. In this letter, a fuzzy clustering algorithm based on gamma distribution for SAR image segmentation is proposed, in which the Stirling equation is used to approach the gamma function in the dominator of gamma distribution under the assumption of mean field theory to make the shape parameter $\alpha $ derivable. Then, the value range of the estimated $\alpha $ is demonstrated to meet the requirement of gamma distribution by Jensen’s inequality. Experimental results show that the proposed method gives promising results in SAR image (including large area SAR image) segmentation and effectively suppresses the influence of speckle.

With powerful feature representations, convolutional neural networks (CNNs) have produced tremendous achievements in image classification tasks and, typically, entail millions of labeled samples to train massive parameters. However, the sample labeling of synthetic aperture radar (SAR) images is extremely difficult, especially pixelwise labels, and has, sometimes, required field trips to accomplish labeling. Moreover, the inherent speckle noise may weaken the ability of networks to extract effective features from SAR images. In this article, we address these issues by labeling a few patchwise samples and propose Ridgelet-Nets with speckle reduction regularization for SAR image scene classification by combining deep learning with multiscale geometric analysis and statistical modeling of SAR images. First, we design Ridgelet-Nets with convolutional kernels constructed by ridgelet filters to reduce the training parameters and learn more discriminative features. Then, we embed speckle reduction regularization in the Ridgelet-Nets to restrain the influence of speckle noise and smooth the classification maps, in which the prior information of SAR image statistical modeling is introduced. Finally, we propose an adaptive SAR image scene classification framework based on an extended hierarchical visual semantic model, considering the differences in the structures and spatial relationships of different regions in the SAR images, particularly large-scale and complex scenes. Experimental results on real SAR images demonstrate that the proposed framework can achieve preferable classification performance using very limited labeled samples.

Synthetic aperture radar (SAR) typically faces both large-scale data and speckle noise problems, which, respectively, induce enormous strains on transmission and storage and interfere with the analysis and interpretation of SAR images. To tackle these, we present a real-time processing deep network, called SPB-Net, to implement imaging and speckle suppression simultaneously. First, a novel imaging-despeckling observation model with the nonlogarithmic additive speckle noise is established. Subsequently, guided by the statistical properties of noise and sparse and detail-preserved requirements in SAR imaging and despeckling, we formulate an $L_{2}$ along with two $L_{1}$ regularizations as the fidelity, sparse, and image detail-preserved constraints, respectively. Convolution layers are employed to improve the feature representation capability in the latter $L_{1}$ term as well. Based on this, we construct a corresponding convex optimization problem. Then, the complex-valued split Bregman method, focusing on the complex-variable convex problem, is unfolded into a parameter-learnable and architecture-fixed SPB-Net to solve the proposed problem effectively and efficiently. Experimental results with the downsampled Radarsat-1 raw data demonstrate the validity in imaging and speckle suppression and the real-time processing capability of the proposed SPB-Net.

Measurements in remote sensing geodesy have been well known to be of speckle noise nature. Although a number of despeckling algorithms have been proposed mainly based on the local weighted statistics in the engineering literature, there are relatively few studies on the statistical adjustment methods for processing the measurements contaminated with the speckle or multiplicative errors. We develop the least squares (LS)-based adjustment methods for the remote sensing measurements with multiplicative errors and trends, evaluate the accuracy of the parameter estimates, and derive the corresponding formulas to estimate the variance of the unit weight. Simulation examples are used to illustrate the developed theory and methods.

Computer-generated holographic (CGH) displays show great potential and are emerging as the next-generation displays for augmented and virtual reality, and automotive heads-up displays. One of the critical problems harming the wide adoption of such displays is the presence of speckle noise inherent to holography, that compromises its quality by introducing perceptible artifacts. Although speckle noise suppression has been an active research area, the previous works have not considered the perceptual characteristics of the Human Visual System (HVS), which receives the final displayed imagery. However, it is well studied that the sensitivity of the HVS is not uniform across the visual field, which has led to gaze-contingent rendering schemes for maximizing the perceptual quality in various computer-generated imagery. Inspired by this, we present the first method that reduces the "perceived speckle noise" by integrating foveal and peripheral vision characteristics of the HVS, along with the retinal point spread function, into the phase hologram computation. Specifically, we introduce the anatomical and statistical retinal receptor distribution into our computational hologram optimization, which places a higher priority on reducing the perceived foveal speckle noise while being adaptable to any individual's optical aberration on the retina. Our method demonstrates superior perceptual quality on our emulated holographic display. Our evaluations with objective measurements and subjective studies demonstrate a significant reduction of the human perceived noise.

Abnormal atrioventricular valve present great challenges to the surgeon in achieving a successful repair, and thus present a great opportunity for enhanced 3D imaging to guide pre- and intra-operative management. Spatial and temporal resolution of 3D echocardiography enables 3D printing of valve morphology. However, non-linearity, angle dependence, speckle, blur, and resampling complicate segmentation compared to computed tomography (CT) and magnetic resonance imaging (MRI). A case of complex mitral valve disease in a pediatric patient is therefore presented to illustrate the technical challenges of segmentation and 3D printing from echocardiographic data.

A conventional three-dimensional digital image correlation (DIC) based on painted speckle patterns has been used widely as a non-contact measurement method to study modal analysis of various structures. Surface treatment using painted speckle patterns might change the structural properties of flexible and lightweight structures such as artificial wings of flapping micro air vehicles. Furthermore, if materials and structures are being serviced, it is essential that their surfaces not be contaminated. We propose a new DIC method that is capable of preventing effects of painted speckle patterns on thin flexible structures during vibration measurement. We project a virtual speckle pattern onto the surface of structures instead of using painted speckle patterns. For a benchmark test, we demonstrate the effectiveness of the proposed virtual speckle pattern DIC method for measuring the structural characteristics of a cantilever beam. We then apply the proposed DIC method to the vibration measurement of an artificial flapping wing. Finite element analysis (FEA) of the artificial wing is performed in ABAQUS™ to obtain natural frequencies and mode shapes. The natural frequencies at the first three modes of the artificial wing obtained from the proposed method are higher than those obtained from the conventional 3D DIC because of the smaller weight of the unpainted wing. The results of the mass compensation, laser sensor, and finite element analysis prove the effectiveness of the proposed virtual speckle pattern DIC method. The proposed DIC method achieves high accuracy and prevents the effect of painted speckle patterns on the dynamic vibration measurement of a thin flexible structure such as an artificial flapping wing.

Ultrasound is the most widely used biomedical imaging modality for the purpose of diagnosis. It often comes with speckle that results in reduced quality of images by hiding fine details like edges and boundaries, as well as texture information. In this present study, a novel wavelet thresholding technique for despeckling of ultrasound images is proposed. For analysing performance of the method, it is first tested on synthetic (ground truth) images. Speckle noise with distinct noise levels (0.01–0.04) has been added to the synthetic images in order to examine its efficiency at different noise levels. The proposed technique is applied to various orthogonal and biorthogonal wavelet filters. It has been observed that Daubechies 1 gives the best results out of all wavelet filters. The proposed method is further applied on ultrasound images. Performance of the proposed technique has been validated by comparing it with some state-of-the-art techniques. The results have also been validated visually by the expert. Results reveal that the proposed technique outperforms other state-of-the-art techniques in terms of edge preservation and similarities in structures. Thus, the technique is effective in reducing speckle noise in addition to preserving texture information that can be used for further processing.

This research aimed to study a polarized laser speckle contrast imaging (LSCI) system to assess the pulp blood flow, where the ability of vertically polarized LSCI (vp-LSCI) and the non-polarized L...

Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging procedure is inevitably contaminated by heavy speckle noise, which impairs both visual quality and diagnosis of various ocular diseases. Although deep learning has been applied for image denoising and achieved promising results, the lack of well-registered clean and noisy image pairs makes it impractical for supervised learning-based approaches to achieve satisfactory OCT image denoising results. In this paper, we propose an unsupervised OCT image speckle reduction algorithm that does not rely on well-registered image pairs. Specifically, by employing the ideas of disentangled representation and generative adversarial network, the proposed method first disentangles the noisy image into content and noise spaces by corresponding encoders. Then, the generator is used to predict the denoised OCT image with the extracted content features. In addition, the noise patches cropped from the noisy image are utilized to facilitate more accurate disentanglement. Extensive experiments have been conducted, and the results suggest that our proposed method is superior to the classic methods and demonstrates competitive performance to several recently proposed learning-based approaches in both quantitative and qualitative aspects. Code is available at: https://github.com/tsmotlp/DRGAN-OCT .