Optical Image Processing Research Articles

Infrared star image denoising using regions with deep reinforcement learning

Efficient and accurate extraction and restoration of star targets in infrared star images with small number of frames is a growing need for optical adaptive image processing. Among the various noise in star images, mixed Poisson-Gaussian noise is difficult to be accurately suppressed due to its complicated distribution function. Aiming at obtaining the true value of star targets’ signal intensity in infrared images, a novel star target extraction and denoising model called regions with deep reinforcement learning (RDRL) is designed and developed in this study. This fully-automatic model contains two modules: (1) star region extraction module (SREM) that generates star regions within the image through an iterative algorithm based on geometric centroid method (GCM); (2) denoising module that performs an iterative denoising process on the star regions based on deep reinforcement learning. The denoising algorithm is tested on infrared star images, and the experiment results indicate that the proposed RDRL denoising model is able to achieve more accurate restoration with a smaller number of calculations than existing star image denoising methods.

Airborne optical polarization imaging for observation of submarine Kelvin wakes on the sea surface: Imaging chain and simulation

The interaction between a moving submarine and seawater generates characteristic wakes on the sea surface, enabling indirect detection of undersea objects via airborne remote sensing. Here, we demonstrate the feasibility of using visible-light polarization imaging to observe submarine wakes. The key links in the imagine chain are considered separately. These include the polarization patterns of skylight, the elevations and slopes of submarine wakes and sea waves, and the changes in the sea surface polarized bidirectional reflectance characteristics due to modulation of gravity–capillary waves by the wake’s velocity field. A complete model of the airborne optical polarization imaging process is constructed and images are simulated via ray tracing. All theories proposed are verified by a series of terrestrial observation experiments. The results show that both the sea surface roughness modulation by the wake’s velocity field and the sea surface slope formed by wake elevation play significant roles in the imaging process. The wake features in the Stokes vector linear polarization component (Q, U) images are effectively enhanced, and the environmental adverse effect on these images is smaller than that on the intensity images. The degree of linear polarization (DoLP) and angle of polarization (AoP) images exhibit acceptable contrast under certain zenith and azimuth angles. Thus, our analysis confirms that airborne optical polarization imaging has considerable potential for observing wakes and other small- and medium-scale ocean dynamic processes.

High-quality reconstruction of an optical image by an efficient Laguerre-Gaussian mode decomposition method

We propose and experimentally demonstrate an optical image reconstruction in the Laguerre-Gaussian (LG) domain. Because LG mode features circular symmetry, it has unique advantages to decompose an image with rotational symmetry. In the experiment, we realize a high-fidelity reconstruction of an optical image by using about 104 LG modes. In the proposed configuration, the radial sampling points are much fewer than those in the existing methods, which can significantly improve the efficiency. Furthermore, we show azimuthal image noise reduction through LG domain filtering. Our results open a door for LG-mode based optical image processing.

Influence of Automated Fiber Placement Parameters on Thermoplastic Composite Blanks Used on Stamp Forming Process

In recent years, advanced manufacturing processes have been developed to increase the speed of production in order to reduce production costs. At the scale of thermoplastic composites, the translation is the combination of advanced manufacturing processes. The focus in this study is more specifically on the coupling of automated lay-up (AFP) and stamp forming processes. To date, a consolidation process, such as press-consolidation of thermoplastic composites, obtained blanks. Several trials have begun using an automated fiber placement consolidation to reduce manufacturing time and use unidirectional material. However, the combination of AFP and stamp forming is useful if it optimizes this process without the blank’s full consolidation, which by resulting reduces the manufacturing time. This study estimates blank characteristics through thermal history imposed by a more rapid manufacturing process. A set of blanks with varying process parameters is produced to investigate the influence at the microscopic scale. The interface behaviour is observed with optical microscope and image processing. A statistical study applied to the process is carried out in order to relate the material observations to the input parameters. The results of this study are used for the study of the next process of the combination: the stamp forming.

Dynamic Image Forensics and Forgery Analytics using Open Computer Vision Framework

The key advances in Computer Vision and Optical Image Processing are the emerging technologies nowadays in diverse fields including Facial Recognition, Biometric Verifications, Internet of Things (IoT), Criminal Investigation, Signature Identification in banking and several others. Thus, these applications use image and live video processing for facilitating different applications for analyzing and forecasting." Computer vision is used in tons of activities such as monitoring, face recognition, motion recognition, object detection, among many others. The development of social networking platforms such as Facebook and Instagram led to an increase in the volume of image data that was being generated. Use of image and video processing software is a major concern for Facebook because the photos and videos that people post to the social network are doctored images. These kind of images are frequently cited as fake and used in malevolent ways such as motivating violence and death. You need to authenticate the questionable images before take action. It is very hard to ensure photo authenticity due to the power of photo manipulations. Image formation can be determined by image forensic techniques. The technique of image duplication is used to conceal missing areas.

A joint change detection method on complex-valued polarimetric synthetic aperture radar images based on feature fusion and similarity learning

ABSTRACT Existing deep learning-based change detection methods in the field of polarimetric synthetic aperture radar (POLSAR) usually directly deal with intensity images. Methods can be easily transferred from optical image processing to synthetic aperture radar (SAR) image processing. However, the polarization information, which is critical in POLSAR data, has been discarded under normal situations. This paper introduces a novel joint change detection network based on similarity learning for coregistered complex-valued SAR data. A pseudo-siamese network takes both amplitude information and polarization information of POLSAR data as the input. The fusion of low-level, middle-level and high-level features enables the network to keep high-resolution and have strong representation ability during training procedures. Our novel sub-networks, which we term C2-Net and Intensity-Net, deal with the covariance matrix of complex SAR data and amplitude SAR data, respectively. The Intensity-Net works as a typical classification network and detects targets directly. The C2-Net attempts to find the relationship between two SAR data patches. An improved cosine similarity function is used to measure the similarity between two generated feature vectors in C2-Net. Output probability vectors of the two sub-networks are combined for final change detection. The two sub-networks are trained jointly and simultaneously. Control experiments show that proposed improvements are working. Experimental results on dual-polarization complex-valued SAR data from Sentinel-1 demonstrate the effectiveness of the proposed method.

Quantitative assessment of retinal and choroidal blood vessels volume using a voxel processing of optical coherence tomography angiography images

Recently, we have proposed a voxel processing algorithm of optical coherence tomography angiography (OCTA) images to increase the accuracy of quantitative assessment of retinal and choroidal blood vessels volume. The purpose of this is to ascertain the effectiveness of the algorithm and to use it for determining the volumetric flow index ratio of the vascular network in the choroid (VChor) and the total vascular network (VAll) across the retinal and choroidal layers. Five patients (seven eyes) aged 25 to 68 years without ophthalmic pathologies and nine patients (12 eyes) aged 64 to 79 years with stage II of primary open angle glaucoma (POAG) were given diagnostic OCTA tests (Spectralis OCT2, Heidelberg Engineering, Germany), each consisting of 512 sagittal scans of the peripapillary area. Testing the feasibility of the method showed that the difference between the values of VChor / VAll ratio in two successive OCTA tests of the same patient’ eye does not exceed 5%, whereas the difference is significant between subjects without eye pathologies and patients with POAG (p < 0.05 according to Mann Whitney U test). The voxel processing algorithm proved useful in quantitative assessment of the volumetric flow index of retinal and choroidal blood vessels and in the estimation of the VChor / VAll—the share of choroidal blood vessels volume in the total blood vessels volume within the peripapillary “retina-choroid” layers.

Use of smartphones as optical metrology tools for surface wear detection

Proper wear level information and early wear detection are crucial goals in many engineering applications and industrial components in order to improve efficiency and reduce production, maintenance, or replacements costs. Furthermore, this should ideally be achieved with a user-friendly, low-cost, and easy to implement methodology for wear level monitoring and detection. In this work, we present the design of a new approach to accomplish early wear detection that is implemented by means of a stand-alone smartphone device and application providing real-time online metrology. The online monitoring is done by means of optical measurements and image processing based on the advanced smartphone vision system technology currently available in commercial devices. The developed mobile App works in continuous mode without interrupting the wear process. Specifically, it traces surface changes and monitors the progression of wear enabling just-in-time warning alarms for “significant wear” and “critical wear” detection. We demonstrate that critical wear of a surface prior to fatal rupture can be detected, which is the main objective in many industrial applications.

AN ANALYSIS OF THE LIQUID FILMS BREAK-UP USING THE COMPUTER VISION TECHNIQUE

The aim of this paper is to introduce a novel approach for analysing the droplet formation in a transonic flow. The method suggested in this work is based on the combination of the measured data from the wind tunnel by an optical measurement and image processing. A new wind tunnel was developed for the study of the liquid film atomization at high speed flows similar to ones that can be found in steam turbines. The coarse droplets in steam turbines are formed from the liquid films on the blades and inner casings. The coarse droplets formed on the stator blades don´t follow the bulk flow and collide with the following moving blades. These collisions cause erosion and corrosion processes, which have an unfavourable effect on the reliability and the efficiency of the low pressure stages of steam turbines. The tunnel is equipped with a standard instrumentation for the measurement of the flow properties and for the analysis of the size distribution of the droplets. Two measurement methods were used for the measurement of the size of the droplets, photogrammetry and light scattering. In this paper, the image processing of the captured images is discussed. The images were taken by a camera with a telecentric lens. The paper contains an assessment of three image processing methods used for the measurement of the droplet distribution by the light extinction. Moreover, the formation of the droplets is captured on the trailing edge of the profile in the flow. The results bring a new view on the formation of droplets at high speeds.

Heartbeat optical coherence elastography: corneal biomechanics in vivo

.Significance: Mechanical assessment of the cornea can provide important structural and functional information regarding its health. Current clinically available tools are limited in their efficacy at measuring corneal mechanical properties. Elastography allows for the direct estimation of mechanical properties of tissues in vivo but is generally performed using external excitation force.Aim: To show that heartbeat optical coherence elastography (Hb-OCE) can be used to assess the mechanical properties of the cornea in vivo.Approach: Hb-OCE was utilized to detect Hb-induced deformations in the rabbit cornea in vivo without the need for external excitation. Furthermore, we demonstrate how this technique can distinguish corneal stiffness between untreated (UT) and crosslinked (CXL) tissue.Results: Our results demonstrate that stiffness changes in the cornea can be detected using only the Hb-induced deformations in the cornea. Additionally, we demonstrate a statistically significant difference in strain between the UT and CXL corneas.Conclusions: Hb-OCE may be an effective tool for assessing the mechanical properties of the cornea in vivo without the need for external excitation. This tool may be effective for clinical assessment of corneal mechanical properties because it only requires optical coherence tomography imaging and data processing.

Edge Detection with Mie-Resonant Dielectric Metasurfaces

We demonstrate an optical image processing technique in the form of edge detection of an object by exploring the angular selectivity of Mie-resonant dielectric metasurfaces of hexagonal symmetry. We examine the effect of both electric and magnetic Mie-type resonances on the spatial dispersion and reveal that the magnetic resonance provides better edge detection. We also explore the polarization sensitivity of the edge detection process and show that edge detection in our hexagonal metasurface is polarization independent. Our results shed light on the underlying mechanisms of edge detection with resonant dielectric metasurfaces and open new opportunities for ultracompact optical image processing devices having multiple applications in vision and microscopy.

Inside the ensemble: unlocking the potential of one-at-a-time experiments with lab-on-a-chip automation.

The advent of technologies that allow the interactions of individual microscopic particles to be probed "one-at-a-time" has paved the way for new experimental avenues of enquiry in colloidal systems. For example, investigating whether a particular pair of colloidal particles isolated from a macroscopic sample might adhere to each other when brought into close proximity is certainly possible. However, given the probabilistic nature of the process (different particles within the ensemble may have slightly different surface charge distributions and asperities, and interaction energies involved can be close to thermal values), it is important that many hundreds or thousands of pairs of particles are tested under each set of experimental conditions of interest. Currently it is still an arduous task to perform such an experiment a sufficient number of times in order to acquire a data-set that truly represents the ensemble. Herein an automated particle collider for measuring particle-particle interactions has been realized by combining elements of microfluidics, holographic optical tweezers and image processing. Each individual measurement consists of confining two particles within a predetermined chemical micro-environment, and observing whether their interactions lead to aggregation. To automate the measurements, computer software consisting of LabVIEW and Red Tweezers with a custom plugin was used. Preliminary experiments carried out using 1 μm diameter polystyrene particles demonstrated that many hundreds of pairwise-interaction measurements could be carried out autonomously within a matter of hours. Further exemplar real-world experiments, designed to examine the stickiness of emulsion drops as a function of bulk measurements of the ζ-potential (zeta potential) of the sample, were then performed. It is envisaged that such robust approaches to the automation of "one-at-a-time" experiments will find applications in a large number of areas, and enable previously unthinkable experiments to be carried out in a timely fashion, thus allowing the focus to shift away from tedious experimental frustrations to more profound scientific questions.

Architecture of an object-oriented software implementation of algorithms for a full cycle of optical images of an unknown object processing

Formulation of the problem. In the process of software processing of highly distorted optical images (OI) of distant objects formed in ground-based telescopes, it becomes necessary to solve the problems of estimating the parameters of background radiation, filtering images of information objects, isolating them on the original OIs, and classifying them. This situation is typical when processing images of both a single object and moving space objects in images of the starry sky. In this regard, the authors, within the framework of the object-oriented approach, have developed a universal design pattern that makes it possible to implement the necessary processing from a single point of view.Objective. Development of a prototype for software implementation of algorithms for solving the aforementioned problems within the framework of the design pattern proposed by the authors. Examining the possibility of its use in the development of software architecture, with examples of OI processing obtained in natural conditions.Results. The article presents the results of the development of the software implementation algorithms architecture for processing distorted OIs of remote space objects formed in ground-based telescopes. Within the framework of the objectoriented programming paradigm using the Universal Modeling Language (UML), a description of the main components of static and dynamic architecture models is provided. For the static model, a new representation of the OI is proposed using a set of Boolean matrices. Within the framework of the dynamic model, a universal template (interface) has been developed for the implementation of algorithms for processing distorted OIs using UML activity diagrams. Its particularization is given when solving the problems of highlighting individual structural elements in the image of a single object and highlighting the tracks of moving objects in the image of the starry sky.Practical significance. The developed software implementations of low-quality optical image processing algorithms can be used in the modernization of software systems for ground-based telescopes.

Current Status of Tissue Clearing and the Path Forward in Neuroscience.

Due to the complexity and limited availability of human brain tissues, for decades, pathologists have sought to maximize information gained from individual samples, based on which (patho)physiological processes could be inferred. Recently, new understandings of chemical and physical properties of biological tissues and multiple chemical profiling have given rise to the development of scalable tissue clearing methods allowing superior optical clearing of across-the-scale samples. In the past decade, tissue clearing techniques, molecular labeling methods, advanced laser scanning microscopes, and data visualization and analysis have become commonplace. Combined, they have made 3D visualization of brain tissues with unprecedented resolution and depth widely accessible. To facilitate further advancements and applications, here we provide a critical appraisal of these techniques. We propose a classification system of current tissue clearing and expansion methods that allows users to judge the applicability of individual ones to their questions, followed by a review of the current progress in molecular labeling, optical imaging, and data processing to demonstrate the whole 3D imaging pipeline based on tissue clearing and downstream techniques for visualizing the brain. We also raise the path forward of tissue-clearing-based imaging technology, that is, integrating with state-of-the-art techniques, such as multiplexing protein imaging, in situ signal amplification, RNA detection and sequencing, super-resolution imaging techniques, multiomics studies, and deep learning, for drawing the complete atlas of the human brain and building a 3D pathology platform for central nervous system disorders.

Facile optical quantification of mercury ion concentration using graphene quantum dot coated filter paper disks

There is a high demand for facile on-site mercury ion quantification methods in water due to mercury's serious toxicity. Graphene quantum dot (GQD), fluorescent under ultra-violet (UV) light, can be easily synthesized and used as a mercury ion detection probe because the brightness of GQD's fluorescence decreases with the increase of the number of mercury ions. We developed a facile mercury (II) ion quantification method using GQD coated filter paper disks in a 3D-printed well array. Changes of GQD's fluorescent brightness dependent on mercury ion concentration were measured via optical microscopy image processing. We also observed the dependency of mercury ion detection sensitivity on the amount of GQD and type of GQDs. Our approach may be easily adapted for hand-held mobile mercury ion quantification devices in the future due to its simplicity and small sample size.

Active Manipulation of The Spin and Orbital Angular Momentums in a Terahertz Graphene-Based Hybrid Plasmonic Waveguide.

Angular momentums (AMs) of photons are crucial physical properties exploited in many fields such as optical communication, optical imaging, and quantum information processing. However, the active manipulation (generation, switching, and conversion) of AMs of light on a photonic chip remains a challenge. Here, we propose and numerically demonstrate a reconfigurable graphene-based hybrid plasmonic waveguide (GHPW) with multiple functions for on-chip AMs manipulation. Its physical mechanism lies in creating a switchable phase delay of ±π/2 between the two orthogonal and decomposed linear-polarized waveguide modes and the spin-orbit coupling in the GHPW. For the linear-polarized input light with a fixed polarization angle of 45°, we can simultaneously switch the chirality (with −ħ/+ħ) of the transverse component and the spirality (topological charge ℓ = −1/+1) of the longitudinal component of the output terahertz (THz) light. With a switchable phase delay of ±π in the GHPW, we also developed the function of simultaneous conversion of the charity and spirality for the circular-polarized input light. In addition, a selective linear polarization filtering with a high extinction ratio can be realized. With the above multiple functions, our proposed GHPWs are a promising platform in AMs generation, switching, conversion, and polarization filtering, which will greatly expand its applications in the THz photonic integrated circuits.

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales.

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as hydrodynamics, ecology and environmental engineering. Modeling bacterial transport in porous environments at different spatial scales is critical to better predict the consequences of bacterial transport, yet current models often fail to up-scale from laboratory to field conditions. Here, we introduce experimental tools to study bacterial transport in porous media at two spatial scales. The aim of these tools is to obtain macroscopic observables (such as breakthrough curves or deposition profiles) of bacteria injected into transparent porous matrices. At the small scale (10-1000 µm), microfluidic devices are combined with optical video-microscopy and image processing to obtain breakthrough curves and, at the same time, to track individual bacterial cells at the pore scale. At larger scale, flow cytometry is combined with a self-made robotic dispenser to obtain breakthrough curves. We illustrate the utility of these tools to better understand how bacteria are transported in complex porous media such as the hyporheic zone of streams. As these tools provide simultaneous measurements across scales, they pave the way for mechanism-based models, critically important for upscaling. Application of these tools may not only contribute to the development of novel bioremediation applications but also shed new light on the ecological strategies of microorganisms colonizing porous substrates.

МЕТОД СТИСНЕННЯ ДАНИХ ДИФРАКЦІЙНИХ ТА ІНТЕРФЕРЕНЦІЙНИХ ЗОБРАЖЕНЬ

In the paper the diffraction and interference images received by numerical simulation and experimentally in solving fundamental and applied problems of photonics. Images are structures with a special intensity distribution formed by the initial field and the optical system. To increase the speed of processing of the image data compression method with further implementation in databases is developed. The method of diffraction and interference images compression is based on the intensity quantization. An algorithm for image quantization has been developed: target intensity values have been determined, which allow setting quantization levels, and data visualization techniques, which determine the threshold values for these levels. The algorithm also contains image segmentation by the size of the minimum size of the topological object. The vicinity of topological object is defined under the conditions of a visual registration form and do not crossing with other regions. The topological objects of the diffraction field determine as the maximum, minimum and zero intensity, and in the interference pattern such topological objects are the maximum, minimum and the region of the band splitting. Important parameters are the average value of the intensity of the whole image - which highlights its overall structure and the average value of the intensity of the local segment. The following results were obtained by compressing data from an 8-bit image in grayscale to 2 bits of color depth are enough for an interference image, and to 3 bits, which are enough for a diffraction image. The quantization differences for diffraction images and interference patterns are shown. Data compression ratios are calculated. On the one hand, the application of the obtained results and recommendations is possible in various fields of medicine, biology and pharmacy , which use laser technology, and on the other hand in the development of separate IT identification of topological objects in the light field, optical image processing and decision support in optical problems.

A simple device to measure bend limit of sheet metals

The analysis of bend fracture limits of sheet metals is important for the automotive industry. For high and mid-ductility materials, current methods are often unreliable due to the difficultly of detecting the initiation of a crack on the outer surface of the bend. Sophisticated techniques that rely on the strain evolution in the material are capable to detect the cracks and establish the bend limits using digital image correlation (DIC) strain measurement systems. However, these techniques are complex and sometimes impractical for industrial use. This study uses a simplified bend test technique that uses optical image processing to detect crack initiation to then determine the bend limits of aluminum alloys using a modified angled line method (MALM). The findings of this study indicate that the optical method is inconsistent in detecting the crack initiation and likely to result in a large deviation in detected the bend limits.

Determination of the Grain Size in Single-Phase Materials by Edge Detection and Concatenation

This paper presents a novel approach for edge detection and concatenation. It applies the proposed method on a set of optical microscopy images of aluminium alloy Al 99.5% (ENAW1050A) samples with different grain size values. The performance of the proposed approach is evaluated based on the intercept method and compared with the manual grain size determination method. Edge detection filters have proven inefficient in grain boundaries’ detection of the presented microscopy images. To some extent only the Canny edge-detection filter was able to compute grain boundaries of lower-resolution images adequately, while the presented method proved to be superior, especially in high-resolution images. The proposed method has proven its applicability, and it implies higher automatisation and lower processing times compared to manual optical microscopy image processing.