Speckle Intensity Research Articles

Hybrid photoacoustic-fluorescence microendoscopy through a multimode fiber using speckle illumination

Advances in wavefront shaping technologies have enabled minimally invasive endoscopy using a single multimode fiber (MMF) and paved way to pushing the penetration depth limit inside scattering tissue. Photoacoustic imaging and fluorescence imaging are two key enabling imaging modalities that provide excellent anatomical and functional contrast, respectively. Combining their unique advantages is desirable; however, doing so in a minimally invasive endoscope has been a challenge. Here, we present an ultrathin hybrid imaging system with a 250 mm cross section based on an optical MMF and an optical fiber hydrophone and capable of combined photoacoustic and fluorescence microscopy. A reference-free calibration, using a set of prerecorded speckle intensity patterns combined with a reconstruction algorithm, enables compressive speckle imaging with optical resolution determined by the fiber numerical aperture.

Efficient glare suppression with Hadamard-encoding-algorithm-based wavefront shaping.

In this Letter, we explore the effectiveness of a Hadamard encoding algorithm (HEA) for efficiently suppressing glare. Both numerical simulations and experimental data show that light intensity decays exponentially with respect to the number of HEA measurements. Specifically, we applied the HEA to reduce the intensity of a single speckle to 4.1% of its original value within only 16 measurements. In contrast, the commonly used genetic algorithm (GA) can reduce the speckle intensity only to 0.2 of its original value even after 1200 measurements. Therefore, the HEA greatly outperforms the previously adopted GA in terms of efficiency. We further show that the HEA is also applicable to suppress the integrated intensity of many speckles.

A trade-off between speckle size and intensity enhancement of a focal point behind a scattering layer

Focusing light through highly scattering materials by modifying the phase profile of the illuminating beam has attracted a great deal of attention in the past decade paving the way towards novel applications. Here we report on a tradeoff between two seemingly independent quantities of critical importance in the focusing process: the size of the focal point obtained behind a scattering medium and the maximum achievable intensity of such focal point. We theoretically derive and experimentally demonstrate the practical limits of intensity enhancement of the focal point and relate them to the intrinsic properties of the scattering phenomenon. We demonstrate that the intensity enhancement limitation becomes dominant when the focusing plane gets closer to the scattering layer thus limiting the ability to obtain tight focusing at high contrast, which has direct relevance for the many applications exploring scattering materials as a platform for high resolution focusing and imaging.

Interpreting Intensity Speckle as the Coherency Matrix of Classical Light

We show that an intensity speckle can be directly interpreted as the properties of incident light - amplitude, phase, polarization, and coherency over spatial positions. Revisiting the speckle-correlation scattering matrix (SSM) method [Lee and Park, Nat. Comm. 7, 13359 (2016)], we successfully extract the intact information of incident light from an intensity speckle snapshot as the form of coherency matrix. The idea is verified experimentally by introducing the peculiar states of light that exhibit uneven amplitude, phase, polarization, and coherency features. We also find substantial practical advantage of the proposed method compared to the conventional coherency matrix measuring techniques such as Stokes polarimetry. We believe this physical interpretation of an intensity speckle could open a new avenue to study and to utilize the speckle phenomenon in vast subfields of wave physics.

Deep Neural Network Regression for Automated Retinal Layer Segmentation in Optical Coherence Tomography Images.

Segmenting the retinal layers in optical coherence tomography (OCT) images helps to quantify the layer information in early diagnosis of retinal diseases, which are the main cause of permanent blindness. Thus, the segmentation process plays a critical role in preventing vision impairment. However, because there is a lack of practical automated techniques, expert ophthalmologists still have to manually segment the retinal layers. In this study, we propose an automated segmentation method for OCT images based on a feature-learning regression network without human bias. The proposed deep neural network regression takes the intensity, gradient, and adaptive normalized intensity score (ANIS) of an image segment as features for learning, and then predicts the corresponding retinal boundary pixel. Reformulating the segmentation as a regression problem obviates the need for a huge dataset and reduces the complexity significantly, as shown in the analysis of computational complexity given here. In addition, assisted by ANIS, the method operates robustly on OCT images containing intensity variances, low-contrast regions, speckle noise, and blood vessels, yet remains accurate and time-efficient. In evaluation of the method conducted using 114 images, the processing time was approximately 10.596 s per image for identifying eight boundaries, and the training phase for each boundary line took only 30 s. Further, the Dice similarity coefficient used for assessing accuracy gave a computed value of approximately 0.966. The absolute pixel distance of manual and automatic segmentation using the proposed scheme was 0.612, which is less than a one-pixel difference, on average.

SAR Speckle Dependence on Ocean Surface Wind Field

A novel physical paradigm is explored in this paper: synthetic-aperture radar (SAR) ocean speckle is informative. This paper experimentally analyzes the SAR ocean speckle intensity K-distribution model versus sea-surface wind field. It is shown that the normalized intensity moments of the K-distribution measured from actual C-band SAR data well fit the theoretical ones under different wind conditions, i.e., wind regime and relative SAR-wind azimuth direction. In addition, it is observed that the K-distribution shape parameter decreases as wind speed increases. A sensitivity of the K-distribution shape parameter on incidence angle and wind direction is also observed, where the largest variability is experienced at higher incidence angles and under crosswind relative azimuth angle.

Imaging Through Turbid Media With Vague Concentrations Based on Cosine Similarity and Convolutional Neural Network

Underwater imaging has been extensively studied to bypass the limitation aroused by scattering and absorption of water solutions. It is highly meaningful to the development of optical imaging, especially in turbid media. The existing methods developed for reconstruction of original images from speckle patterns are applied in a stable medium, which obstruct wider applications in unpredictable media. Hence, it is crucial to take changeable environments into consideration to circumvent the limits of the extant methods. In this paper, we propose a new approach based on cosine similarity for speckle classification and convolutional neural network (CNN) for the reconstruction. The targets are placed in variant densities of turbid water mixed with certain of milk, and their corresponding intensity speckle patterns are recorded by a camera. It is verified that utilization of cosine similarity for the classification of patterns recorded in changeable media ensures high fidelity for label predictions. For a speckle pattern obtained in totally unidentified media, it can make a prediction of the density which has a high probability of accuracy. We can exploit the classified density to automatically select the most appropriate datasets to train a CNN model and then make predictions in real time with the trained CNN model. The combined model presented in this paper is tolerant to the uncertainty of turbidity. Moreover, it guarantees high-accuracy pattern classification and high-quality image reconstruction. It is feasible for potential applications in harsh water solutions with unknown perturbations of concentrations.

Comprehensive Zakharov-type model for parametric instabilities in the corona of direct-drive targets

We report on two-dimensional simulations of parametric instabilities excited by a single, intense (2 × 1016 W cm−2), wavelength-sized laser speckle immersed in a nonuniform CH plasma close to the quarter-critical density. A first set of simulations is performed using a Zakharov-type reduced model. This newly developed simulation tool, which we present in detail, describes Stimulated Raman Scattering (SRS), Two-Plasmon Decay (TPD), Stimulated Brillouin Scattering, and secondary decays involving ion acoustic waves. Because of the high speckle intensity considered, strong electron plasma waves (EPWs) are driven via TPD, whose beating induces fast-collapsing ion cavities. Yet, Zakharov equations are notoriously incapable of modeling collapse arrest due to their neglect of kinetic processes dissipating energy from small-scale EPW packets, thus resulting in nonphysical density evolution. Transit-time damping is such a process, and we show that its inclusion in the reduced model allows for a self-consistent description of all phases of the collapse. The accuracy of our model is checked against a second simulation, performed using a particle-in-cell (PIC) code run under similar conditions. Good qualitative agreement is obtained, particularly in regard to the cavities' dynamics. Still, an excessive SRS reflectivity is predicted by the reduced model, which we ascribe to missing kinetic effects. The influence of electron trapping and heating on the decay of EPWs and saturation of SRS is assessed based on the PIC simulation.

Quantitative Phase Imaging Camera With a Weak Diffuser

We introduce the quantitative phase imaging camera with a weak diffuser (QPICWD) as a practical realization of quantitative phase imaging (QPI) on standard microscope platforms. The QPICWD is a compact stand-alone camera which measures sample induced phase delay under low-coherence quasi-monochromatic illumination by examining the deformation of the speckle intensity pattern. By interpreting the speckle deformation with an ensemble average of the geometric flow, we can obtain the high-resolution distortion field by solving the transport of intensity equation (TIE). Since the phase measured by TIE is the generalized phase of the partially coherent image, instead of the phase of the measured object, we analyze the effect of illumination coherence and imaging numerical aperture (NA) on the accuracy of phase retrieval, revealing that the phase of the object can be reliably retrieved when the coherence parameter (the ratio of illumination NA to objective NA) of the Kohler illumination is between 0.3 and 0.5. We present several applications for the new design including nondestructive optical testing of microlens array with nanometric thickness and imaging of fixed and live HeLa cells. Since the designed QPI camera does not require any modification of the widely available bright-field microscope or additional accessories for its use, it is expected to be adopted by the broader biology and medical community.

Enhancement of spatial resolution in digital holographic microscopy using the spatial correlation properties of speckle patterns

In this study, we demonstrate enhancement of the spatial resolution in digital holographic microscopy (DHM) using the spatial correlation properties of speckle patterns. In this method, the spatial correlation coefficients of speckle intensity are controlled by illuminating a diffuser, such as a ground glass plate, with an illumination spot with intensity profiles of a ring shape produced by an amplitude-modulated aperture. These speckle patterns are incident on an object to achieve a higher numerical aperture of the illumination system in DHM. The theoretical predictions and experimental results show that higher spatial resolution in DHM can be realized by adjusting the spatial correlation properties of speckle patterns.

Segmentation of sonar imagery using convolutional neural networks and Markov random field

In this paper, we present a novel method incorporating convolutional neural networks (CNN) into Markov random field (MRF) to automatically segment side scan sonar (SSS) images into object-highlight, object-shadow and sea-bottom reverberation areas. As a widely used ocean survey sensor, SSS provides high-resolution maps of the seafloor. Automatically segmenting SSS in real time can assist the navigation and path-planning of autonomous underwater vehicles. However, for the speckle noise and intensity inhomogeneity in the SSS images, it is difficult to find a robust SSS segmentation method. These facts motivate us to explore efficient CNN architectures to solve these problems. For pixel-level SSS segmentation, to use the context information and the details around a central pixel simultaneously, the CNN with multi-scale inputs (MSCNN) is employed. Besides, to mitigate the impact of the class imbalance problem, two MSCNN training strategies are introduced, which are based on data augmentation and ensemble learning. Furthermore, to take into account the local dependencies of class labels, the results of MSCNN are used to initialize MRF to get the final segmentation maps. Experimental results on real SSS images reveal that the proposed segmentation method outperforms MRF, CNN and semantic segmentation methods such as fully convolutional network and Segnet in segmentation accuracy and generalization performance. Moreover, the efficiency of the proposed method is proved on retinal image dataset.

ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation.

This paper presents a novel and practical convolutional neural network architecture to implement semantic segmentation for side scan sonar (SSS) image. As a widely used sensor for marine survey, SSS provides higher-resolution images of the seafloor and underwater target. However, for a large number of background pixels in SSS image, the imbalance classification remains an issue. What is more, the SSS images contain undesirable speckle noise and intensity inhomogeneity. We define and detail a network and training strategy that tackle these three important issues for SSS images segmentation. Our proposed method performs image-to-image prediction by leveraging fully convolutional neural networks and deeply-supervised nets. The architecture consists of an encoder network to capture context, a corresponding decoder network to restore full input-size resolution feature maps from low-resolution ones for pixel-wise classification and a single stream deep neural network with multiple side-outputs to optimize edge segmentation. We performed prediction time of our network on our dataset, implemented on a NVIDIA Jetson AGX Xavier, and compared it to other similar semantic segmentation networks. The experimental results show that the presented method for SSS image segmentation brings obvious advantages, and is applicable for real-time processing tasks.

Statistical properties of dynamic speckles in application to laser focusing systems.

Dynamic speckles, which carry information about beam parameters of a diffuse object, are produced by a moving diffuse object under illumination of a Gaussian beam. In this paper, we consider that the diffuse object moves in a plane with constant velocity and discuss the statistical properties of dynamic speckles for estimating the variation of focusing spot size. The space-time statistical properties of dynamic speckle have been revealed by analyzing the space-time cross-correlation function of speckle intensity fluctuations detected at two points in the receiving plane. We discuss the influence of the distance between two point detectors on the detection results by simulation analyses, and the theoretical analysis results are verified by experiment. This method, which applies feedback of the dynamic speckle fields for estimating the variation of focusing spot size, will help a laser focusing system optimize focusing performance.

Comprehensive Coagulation Profiling at the Point-of-Care Using a Novel Laser-Based Approach.

Delays in identifying internal bleeding are life-threatening, thus underscoring the need for rapid and comprehensive coagulation profiling at the bedside. The authors review a novel optical coagulation profiler that measures several coagulation metrics including prothrombin time, activated clotting time, clot polymerization rate (α-angle), clot stiffness (maximum amplitude), fibrinolysis (LY), and platelet function, using a single multifunctional instrument. The optical profiler is based on the principles of Laser Speckle Rheology that quantifies tissue viscoelasticity from light scattering patterns called laser speckle. To operate the optical profiler, whole blood (40 μL) is loaded into a disposable cartridge, laser speckle patterns are recorded via a camera, and the viscoelasticity of clotting blood is estimated from speckle intensity fluctuations. By monitoring alterations in viscoelastic moduli over time during clot initiation, thrombin generation, fibrin crosslinking, clot stabilization, and LY, global coagulation parameters are obtained within 10 minutes using a drop of whole blood. Clinical testing in over 500 patients to date has confirmed the accuracy of the optical profiler for comprehensively assessing coagulation status against conventional coagulation tests and thromboelastography. Recent studies have further demonstrated the capability to quantify platelet aggregation induced by adenosine diphosphate in a drop of platelet-rich-plasma in the absence of applied shear stress. Together, these studies demonstrate that global coagulation profiling in addition to platelet function may be accomplished using a single multifunctional device. Thus, by enabling rapid and comprehensive coagulation and platelet function profiling at the bedside, the optical profiler will likely advance the capability to identify and manage patients with an elevated risk for hemorrhage.

Introducing non-local correlations into laser speckles.

Laser speckles have become a fundamental component of the modern optics-research toolbox. Not only are speckle patterns the basis of numerous imaging techniques, but also, they are employed to generate optical potentials for cold atoms and colloidal particles. The ability to manipulate a speckle pattern's spatial intensity correlations, particularly long-range (non-local) ones, is essential in numerous applications. A typical fully-developed speckle pattern, however, only possesses short-ranged (local) intensity correlations which are determined by the spatial field correlations. Here we experimentally demonstrate and theoretically develop a general method for creating fully-developed speckles with strong non-local intensity correlations. The functional form of the spatial intensity correlations can be arbitrarily tailored without altering the field correlations. Our approach provides a versatile and utilitarian framework for enhancing and controlling non-local correlations in speckle patterns.

Assessment of multifractal analysis in dynamic laser speckle signals using a spatial light modulator.

We assess and discuss several technical aspects of the multifractal statistical analysis applied to time series of dynamic speckle intensity signals. Due to the complexity of this goal, we implemented an optical setup that mimics the light scattering effect from the illuminated object using a spatial light modulator. The multifractal spectrum of the obtained dynamic speckle intensity signals is quantified by utilizing a mathematical framework based on the decomposition of wavelet leaders' functions. The propagated light that is scattered utilizing the spatial light modulator verifies the well-known first- and second-order statistics of the obtained speckle images and also a given temporal correlation function determined by a copula algorithm adding several classes of fractional Gaussian noises. To experimentally implement these issues, we load appropriate dynamic temporal phase screens in the spatial light modulator working on phase-only mode and guide the light propagation through an optical setup composed of a 4f correlator. Different types of statistical trends in the scaling properties as a function of frequency sampling, intensity signal discretization, mean size of speckle, temporal correlation length, and vanishing moments of the elected mother wavelet analysis are theoretically and experimentally tested and compared.

The effect of speckles noise on the Laser Doppler Vibrometry for remote speech detection

Speckles noise generated by the relative in-plane motion between the Laser Doppler Vibrometry (LDV) and the target damages the quality of the LDV-captured speech severely. As such, it is necessary to research on the effect of speckles noise on the LDV for remote voice detection and remove it. In this paper, we comprehensively discuss the statistical properties of dynamic speckles with the combination of theories and experiments, and the results show that the fluctuation of speckles intensity depends strongly on the relative velocity for a given value of the detection distance. Then, we investigate the effects of the speckles noise on the LDV for remote audio detection, and the results indicate that speckles noise can cause spikes and bursts appearing in the LDV-captured voice signals, which degrades the quality of the speech. To address this issue, an algorithm of the speckle noise reduction in the LDV-measured audio signals is proposed and the experimental results demonstrate that the proposed algorithm performs well.

A New Insight into Biospeckle Activity in Apple Tissues.

The monitoring and characterization of agricultural products before harvest or during ripening, storage, and shelf life has recently been increasingly explored in the literature. The analysis of biospeckle activity has potential for the determination of the optimal harvest window, the monitoring of the fruit ripening process, and the detection of diseases and bruising. In this technique, the specimen is illuminated with coherent light and speckle intensity fluctuations are analyzed using diverse methodologies. Prior work shows that biospeckle activity is strongly correlated to physiological indexes conventionally used to evaluate fruit texture and composition. Here, we scrupulously investigate the biospeckle activity of Gala apple fruits during postharvest stages. We simulate realistic conditions for shelf-life monitoring, namely an unknown history of the fruit and storage in an uncontrolled atmosphere. Scattering spot images are acquired with multiple exposure times using a simple optical setup. The contrast, reflecting biospeckle activity, is computed after eliminating inhomogeneous zones. The results show, for the first time, speckle activity at short time scales. The retrieved correlations between speckle parameters and the ratio of apples’ firmness to their soluble solids content reveal significant links despite the unknown fruit’s origin, harvest date, and storage history.

Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network?

The sonogram is currently an effective cancer screening and diagnosis way due to the convenience and harmlessness in humans. Traditionally, lesion boundary segmentation is first adopted and then classification is conducted, to reach the judgment of benign or malignant tumor. In addition, sonograms often contain much speckle noise and intensity inhomogeneity. This study proposes a novel benign or malignant tumor classification system, which comprises intensity inhomogeneity correction and stacked denoising autoencoder (SDAE), and it is suitable for small-size dataset. A classifier is established by extracting features in the multilayer training of SDAE; automatic analysis of imaging features by the deep learning algorithm is applied on image classification, thus allowing the system to have high efficiency and robust distinguishing. In this study, two kinds of dataset (private data and public data) are used for deep learning models training. For each dataset, two groups of test images are compared: the original images and the images after intensity inhomogeneity correction, respectively. The results show that when deep learning algorithm is applied on the sonograms after intensity inhomogeneity correction, there is a significant increase of the tumor distinguishing accuracy. This study demonstrated that it is important to use preprocessing to highlight the image features and further give these features for deep learning models. In this way, the classification accuracy will be better to just use the original images for deep learning.

Ship Segmentation in SAR Images by Improved Nonlocal Active Contour Model.

Synthetic aperture radar (SAR) has been widely used in ocean surveillance. As an important part of shipping management and military applications, ship monitoring is a study hotspot in SAR image interpretation; hence, many researches focus on ship targets. Among these studies, ship segmentation is a basic work, but still remains challenging due to the speckle noise and the complicated backscattering phenomenology in SAR images. To solve the problems, this paper proposes a new method for ship segmentation by nonlocal processing. Firstly, the proposed nonlocal energy describes the nonlocal comparison of patches and optimizes regions with spatially-varying features. Secondly, we rewrite the energy functional by introducing a ratio distance defined with respect to the probability density functions of regions to overcome the influence of the multiplicative noise. Finally, the integral histogram is introduced into the pairwise interactions to fasten the speed of convergence. Several rounds of comparative experiments are implemented on real SAR data with different resolutions and bands. The results demonstrate that the proposed method is robust to the speckle noise and intensity variations and could achieve refined segmentation for ship targets.