Optical Image Processing Research Articles

An optimal method for measuring biomarkers: colorimetric optical image processing for determination of creatinine concentration using silver nanoparticles.

Creatinine concentration is one of the important elements in the body for diagnosing kidney failure, muscular dystrophy, glomerular filtration rate, and diabetic nephropathy. The disadvantages of recently introduced analytical techniques, such as Jaffe's, spectroscopic, colorimetric, and chromatographic methods, for quantifying creatinine in urine involve toxicity, the high cost, interference, and the complexity of the design. In this paper, we designed and fabricated a new colorimetric assay for the measurement of creatinine concentration based on color differentiation generated by mixing different concentrations of creatinine with synthesized silver nanoparticles (AgNPs) coated with polyvinylpyrrolidone (PVP) and polyvinyl alcohol (PVA). An isolated box is designed for the uniform optical imaging of solutions, the captured images are processed in real time, and the quantitative and qualitative results are displayed. For colorimetric processing, a variety of color systems, such as RGB (red, green, blue), CMYK (cyan, magenta, yellow, black), and grayscale (Gr), have been evaluated, indicating that the combination of green (G) and grayscale (Gr) provides the best results for this experiment. TEM analysis and spectroscopy were used to confirm the results of the experiment. Linear range and limit of detection (LOD) were obtained for AgNPs/PVP 0.03-1mg/dl and 0.024mg/dl and for AgNPs/PVA 0.01-1mg/dl and 0.014mg/dl, respectively, indicating the superiority of our proposed method over recently introduced methods. In this experiment, the detectable resolution with AgNPs/PVP is 40, while it is 71 with AgNPs/PVA. The designed system is simple to use, small in size, and cost-effective for measuring creatinine concentration, while it can be used as a portable system.

Cloud Detection for Satellite Imagery Using Deep Learning

Cloud is the most uncertain factor in the climate system and has a huge impact on climate change. Therefore, the study of changes in cloudiness is of great importance for understanding climate and climate change. Cloud detection is also an important research area in satellite remote sensing image pre-processing. But cloud detection is a difficult task due to various noise disturbances in the remote sensing data itself, as well as factors such as ice and snow on the ground. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in methods such as image processing and classification. In this study, we use the modified U-Net architecture that introduces the attention mechanism for cloud detection. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method, especially in snowy areas and other areas covered by bright non-cloud objects. The effectiveness of this method makes it a great potential for other optical image processing as well.

Ultrafast Topological Engineering in Metamaterials.

Transient optical heating provides an efficient way to trigger phase transitions in naturally occurring media through ultrashort laser pulse irradiation. A similar approach could be used to induce topological transitions in the photonic response of suitably engineered artificial structures known as metamaterials. Here, we predict a topological transition in the isofrequency dispersion contours of a layered graphene metamaterial under optical pumping. We show that the contour topology transforms from elliptic to hyperbolic within a subpicosecond timescale by exploiting the extraordinary photothermal properties of graphene. This new phenomenon allows us to theoretically demonstrate applications in engineering the decay rate of proximal optical emitters, ultrafast beam steering, and dynamical far-field subwavelength imaging. Our study opens a disruptive approach toward ultrafast control of light emission, beam steering, and optical image processing.

Ultrashort laser pulse doubling by metal-halide perovskite multiple quantum wells

Multiple ultrashort laser pulses are widely used in optical spectroscopy, optoelectronic manipulation, optical imaging and optical signal processing etc. The laser pulse multiplication, so far, is solely realized by using the optical setups or devices to modify the output laser pulse from the optical gain medium. The employment of these external techniques is because the gain medium itself is incapable of modifying or multiplying the generated laser pulse. Herein, with single femtosecond laser pulse excitation, we achieve the double-pulsed stimulated emission with pulse duration of around 40 ps and pulse interval of around 70 ps from metal-halide perovskite multiple quantum wells. These unique stimulated emissions originate from one fast vertical and the other slow lateral high-efficiency carrier funneling from low-dimensional to high-dimensional quantum wells. Furthermore, such gain medium surprisingly possesses nearly Auger-free stimulated emission. These insights enable us a fresh approach to multiple the ultrashort laser pulse by gain medium.

Spatially polarization-modulated ellipsometry based on the vectorial optical field and image processing

We report a new (to the best of our knowledge) ellipsometric measurement scheme called spatially polarization-modulated ellipsometry (SPME), which is based on the vectorial optical field and digital image processing. A zero-order vortex half-wave retarder (ZVHR) is employed to generate the vectorial optical field and analyze the elliptically polarized light reflected by a thin film; further, an analyzer is set after the ZVHR to form an hourglass intensity pattern due to the spatially polarization modulation; then, the film’s ellipsometric angles can be obtained by processing the hourglass intensity image. By analyzing the working principle of SPME, we have found that the film’s ellipsometric angles are determined by the bright areas’ azimuth angle and contrast of the modulated images, and their mathematical relationships have been deduced and provided. To reduce the adverse effects of image noise and improve the measurement precision, an improved operation mode of SPME is presented by processing the modulated images with their bright areas’ azimuth locating at near 45° or 135° and 0° or 90°. Numerical analyzing studies have been carried out on the S i O 2 films to validate the feasibility of SPME, and the simulation experiments indicate that the SPME can operate well, even though obvious noise has been added to the modulated intensity image and the measuring error of film thickness and refractive index were less than 0.1 nm and 0.001, respectively.

Quantitative evaluation of mixing using a refined Shannon entropy

A method to quantify the dispersion level of fillers in a polymer composite using optical microscopy and image processing is demonstrated. Optical microscope images of a graphene suspension were taken throughout an ultrasonication procedure. A mixing index was calculated for each image using a modified Shannon entropy analysis, demonstrating that the index can be used to optimize procedures for dispersing fillers in polymers. The flexibility of the Shannon entropy index is achieved through two user-defined variables based on physically meaningful features, allowing the analysis to be applicable to a wide range of filler size distributions. Calculating a mixing index allows for characterization beyond the typical categories of ‘highly’ and ‘poorly’ dispersed systems. An index can be calculated at various stages of composite processing allowing for robust statistical analysis of the dispersion level, using only a small sample volume. The adoption of this method would lead to quantitative characterization of dispersion level in polymer composites, deepening the understanding of how processing impacts composite properties, and aiding in the design of new composite materials.

Automated identification and characterization of two-dimensional materials via machine learning-based processing of optical microscope images

Mechanical characterization of two-dimensional (2D) materials has always been a challenging task due to their extremely small thickness. The current prevailing methods to measure the strength of 2D materials normally involve sophisticated testing facilities and complicated procedures of sample preparation, which are usually costly and time-consuming. In this paper, we propose a cost-effective and rapid approach to characterizing the strength of 2D materials by processing optical microscope images of the mechanically exfoliated 2D materials. Specifically, a machine learning-based model is developed to automate the identification of 2D material flakes of different layers from the optical microscope images, followed by the determination of their lateral size. The statistical distribution of the flakes’ size is obtained and used to estimate the strength of the associated 2D material based on a distribution-property relationship we developed before. A case study with graphene indicates that the present machine learning-based method, as compared to the previous manual one, enhances the efficiency of characterization by more than one order of magnitude with no sacrifice of the accuracy.

Talbot effect of orbital angular momentum lattices with single photons

The self-imaging, or Talbot Effect, that occurs with the propagation of periodically structured waves has enabled several unique applications in optical metrology, image processing, data transmission, and matter-wave interferometry. In this work, we report on the first demonstration of a Talbot Effect with single photons prepared in a lattice of orbital angular momentum (OAM) states. We observe that upon propagation, the wavefronts of the single photons manifest self-imaging whereby the OAM lattice intensity profile is recovered. Furthermore, we show that the intensity at fractional Talbot distances is indicative of a periodic helical phase structure corresponding to a lattice of OAM states. This phenomenon is a powerful addition to the toolbox of orbital angular momentum and spin-orbit techniques that have already enabled many recent developments in quantum optics.

Plasmonic transmitted optical differentiator based on the subwavelength gold gratings.

A nanoscale plasmonic optical differentiator based on subwavelength gold gratings is investigated theoretically and experimentally without Fourier transform lenses and prisms. In the vicinity of surface plasmon resonance (SPR), the transfer function of subwavelength gold gratings is derived by optical scattering matrix theory. Simulated by the finite difference time domain (FDTD) method, the wavelengths of optical spatial differentiation performed by subwavelength gold gratings are tuned by the grating period and duty cycle, while the throughput of edge extraction is mainly adjusted by the grating thickness. Without Fourier transformation, the fabricated plasmonic optical differentiator experimentally achieves real-time optical spatial differentiation in transmission and implements SPR enhanced high-throughput edge extraction of a microscale image with a resolution of 10 µm at 650 nm, which has potential applications in areas of optical analog computing, optical imaging, and optical information processing.

Towards the Development of Rapid and Low-Cost Pathogen Detection Systems Using Microfluidic Technology and Optical Image Processing

Waterborne pathogens affect all waters globally and proceed to be an ongoing concern. Previous methods for detection of pathogens consist of a high test time and a high sample consumption, but they are very expensive and require specialist operators. This study aims to develop a monitoring system capable of identifying waterborne pathogens with particular characteristics using a microfluidic device, optical imaging and a classification algorithm to provide low-cost and portable solutions. This paper investigates the detection of small size microbeads (1–5 µm) from a measured water sample by using a cost-effective microscopic camera and computational algorithms. Results provide areas of opportunities to decrease sample consumption, reduce testing time and minimize the use of expensive equipment.

Optical observation of plasma bubbles and comparative study of multiple methods of observing the ionosphere over China

Ionospheric plasma bubbles and scintillation phenomena often occur at low latitudes in China. The all-sky airglow imager located at Fuke station in Hainan (19.5°N, 109.2°E) is part of the Meridian project in China. In this work, we used mainly the observation data obtained by the imager and a digisonde to study plasma bubbles. Optical image processing—including image enhancement, azimuth correction and image projection—was performed on the original airglow observation images, and clear high-resolution images of the bubbles were obtained. Based on these, the morphological features, spatial and temporal variations, spatial scale, drift velocity of the plasma bubbles in the F region of the ionosphere were investigated and analyzed. The plasma bubbles appeared as long, dark stripes on the bright airglow emission background. Their large-scale structures often bifurcated vertically, and were usually tilted westward with north-south extension along the magnetic field lines. The east-west scale of the plasma bubbles was usually greater than 200 km and these increased gradually with time. Moreover, the distance between the adjacent plasma bubbles also increased over time, and was in the 100–400 km range. Plasma bubbles generally drifted eastward with an average drift velocity of approximately 90 m/s. The occurrence and duration of the plasma bubbles were compared with digisonde observations, and it was shown that plasma bubbles lasted for nearly four hours. Additionally, the main characteristics of the plasma bubbles were obtained using both optical and radio observations and the study of which can be beneficial for monitoring and predicting ionospheric scintillations.

Analysis of facial redness by comparing VISIA® from Canfield and CSKIN® from Yanyun Technology.

Redness is the most common symptom among many facial dermatoses. With the rapid development of optical instruments, spectral imaging, and image processing technology, there appear varieties of skin color analysis methods and instruments. The aim of this study is to reveal the differences and correlations in measuring the facial redness between CSKIN® and VISIA® , as well as the relevance between the instrument parameters and clinical evaluation. Forty-three Chinese patients were enrolled. Images were taken and analyzed by VISIA® from Canfield and CSKIN® from Yanyun Technology, and the facial erythema was graded by the dermatologists. Feature counts within the red areas measured by VISIA® were found to have significantly positive correlations with red pixels and percent which were measured by CSKIN® on both sides of the face (r=.45~.566, P<.01). The parameters analyzed by CSKIN® and VISIA® feature counts were correlated with visual scores graded by the dermatologists, VISIA® presented with a weak correlation (r=.213, P<.05), while CSKIN® had a moderate correlation with the visual scores (r=.472~.492, P<.001). CSKIN® may be another alternative option when encountering with measurement and follow-up of facial erythema.

Sectioning with edge extraction in optical incoherent imaging processing

Employing a single-pixel digital holographic recording technique called optical scanning holography (OSH), we accomplish the formidable task of sectioning with edge extraction in three-dimensional (3D) optical incoherent imaging. OSH is a special variant of generalized two-pupil heterodyning image processing, where one of the pupils used is a delta function with the other being a uniform function. In this study, we investigate the use of an annular pupil and a random-phase pupil for edge extraction during sectioning of a 3-D object. Novel simulation results indicate excellent edge extraction of a multi-section object with good sectioning capability, i.e., with each focused edge-extracted section out-of-focused haze has been eliminated.

Wavelength-independent optical fully differential operation based on the spin–orbit interaction of light

Optical technology may provide important architectures for future computing, such as analog optical computing and image processing. Compared with traditional electric operation, optical operation has shown some unique advantages including faster operating speeds and lower power consumption. Here, we propose an optical full differentiator based on the spin–orbit interaction of light at a simple optical interface. The optical differential operation is independent of the wavelength due to the purely geometric nature of the phenomenon. As an important application of the fully differential operation, the wavelength-independent image processing of edge detection is demonstrated. By adjusting the polarization of the incident beam, the one-dimension edge imaging at any desirable direction can be obtained. The wavelength-independent image processing of edge detection provides possible applications in autonomous driving, target recognition, microscopic imaging, and augmented reality.

Brewster effect in the broadband light reflectivity

In this work, we analyze the characteristics of the Brewster effect at the air-glass interface for monochromatic and broadband polarized light. The obtained broadband light reflectivity spectrum demonstrates the modified resonance line shape. The effeciency of the Brewster effect for optical image processing is discussed.

Upconversion dark-field imaging with extended field of view at video frame rate.

Dark-field imaging is a well-known optical method for obtaining edge-enhanced images of objects containing steep gradients in either amplitude or phase. Edge enhancement is commonly achieved by using a small physical obstruction in the center of the Fourier plane of a 4f imaging setup. By blocking the low spatial frequencies in the center of the Fourier plane, only the higher spatial frequencies from the object reach the image plane. In this work, simultaneous optical image processing (i.e., dark-field imaging) and sum-frequency generation are performed by placing a periodically poled lithium niobate crystal in the Fourier plane of a 4f setup and using a 1575 nm upconversion pump beam with a dark core. We demonstrate upconversion dark-field (UDF) imaging in which edge-enhanced images are obtained at the upconverted wavelength ($\sim{630}\;{\rm nm}$∼630nm) as a result of infrared object illumination ($\sim 1\;\unicode{x00B5}{\rm m}$∼1µm). Furthermore, we experimentally confirm that UDF imaging can be extended from collinear to noncollinear interactions between the signal and pump. The presented system allows for adjustment of the spatial cutoff frequency while maximizing the power conversion efficiency. Simultaneous collinear and noncollinear upconversion imaging of phase objects is demonstrated using broadband 1 µm illumination with a spectral bandwidth of 6 nm-resulting in a UDF imaging system with an enhanced field of view at video frame rates.

Flat optics for image differentiation

Image processing has become a critical technology in a variety of science and engineering disciplines. Although most image processing is performed digitally, optical analog processing has the advantages of being low-power and high-speed, but it requires a large volume. Here, we demonstrate flat optics for direct image differentiation, allowing us to significantly shrink the required optical system size. We first demonstrate how the differentiator can be combined with traditional imaging systems such as a commercial optical microscope and camera sensor for edge detection with a numerical aperture up to 0.32. We next demonstrate how the entire processing system can be realized as a monolithic compound flat optic by integrating the differentiator with a metalens. The compound nanophotonic system manifests the advantage of thin form factor as well as the ability to implement complex transfer functions, and could open new opportunities in applications such as biological imaging and computer vision. Vertical integration of a metalens to realize compound nanophotonic systems for optical analog image processing is realized, significantly reducing the size and complexity of conventional optical systems.

Centrifugal Pumps as Extractors

AbstractThe generation of dispersions with defined droplet size distributions is important for liquid‐liquid extraction. In this respect, the capability of a centrifugal pump to generate dispersions was investigated for different operating points. For the analysis of the mixing process, a centrifugal pump was designed translucent. High‐speed images reveal the mechanism of particle breakup. Optical measurement technology and image processing with neural networks were used to determine droplet size distributions. The influence of flow rate, rotational frequency and holdup are discussed for a paraffin oil‐water system.

Learning Effective Video Features for Facial Expression Recognition via Hybrid Deep Learning

Facial Expression Recognition is one of the recent trends to detect human expression in streaming video sequences. To identify emotions of video like sad, happy or angry. In this paper, the proposed method employs two individual deep convolution neural networks (CNNs), including a permanent CNN processing of static facial images and a temporary CN network processing of optical flow images, to separately learn high-level spatial and temporal characteristics on the separated video segments. Such two CNNs are fine tuned from a pre-trained CNN model to target video facial expression datasets. The spatial and temporal characteristics obtained at the segment level are then incorporated into a deep fusion network built with a model of deep belief network (DBN). This deep fusion network is used to learn spatiotemporal discriminative features together

PolSAR Image Classification with Lightweight 3D Convolutional Networks

Convolutional neural networks (CNNs) have become the state-of-the-art in optical image processing. Recently, CNNs have been used in polarimetric synthetic aperture radar (PolSAR) image classification and obtained promising results. Unlike optical images, the unique phase information of PolSAR data expresses the structure information of objects. This special data representation makes 3D convolution which explicitly modeling the relationship between polarimetric channels perform better in the task of PolSAR image classification. However, the development of deep 3D-CNNs will cause a huge number of model parameters and expensive computational costs, which not only leads to the decrease of the interpretation speed during testing, but also greatly increases the risk of over-fitting. To alleviate this problem, a lightweight 3D-CNN framework that compresses 3D-CNNs from two aspects is proposed in this paper. Lightweight convolution operations, i.e., pseudo-3D and 3D-depthwise separable convolutions, are considered as low-latency replacements for vanilla 3D convolution. Further, fully connected layers are replaced by global average pooling to reduce the number of model parameters so as to save the memory. Under the specific classification task, the proposed methods can reduce up to 69.83% of the model parameters in convolution layers of the 3D-CNN as well as almost all the model parameters in fully connected layers, which ensures the fast PolSAR interpretation. Experiments on three PolSAR benchmark datasets, i.e., AIRSAR Flevoland, ESAR Oberpfaffenhofen, EMISAR Foulum, show that the proposed lightweight architectures can not only maintain but also slightly improve the accuracy under various criteria.