Optical Image Processing Research Articles

A Parametric Study on the Stability, Geometry and Hardness of AISI 308LSi WAAM-GMAW

Wire arc additive manufacturing (WAAM) has been established to be an efficient and cost-effective additive manufacturing technique for fabricating functional metallic parts from scratch. However, there is need to determine optimal processing condition for each material system to produce high-quality parts. In this work, a parametric study of WAAM of AISI 308LSi was performed to determine the processing condition(s) at which single tracks of high dimensional accuracy, excellent geometry, no visible crack and pore, and high hardness required for high-quality multi-track deposition can be achieved. The track geometries were investigated using a combination of optical microscopy and image processing software. The microstructure and hardness of the deposited single tracks were examined using optical microscopy and Vickers hardness tester respectively. A process map predicting the process stability of WAAM of AISI 308LSi was developed within a process window. Continuous single tracks of high dimensional accuracy were produced from a stable deposition process. The process becomes unstable whenever the wire deposition volume per unit length of track is in excess of the available heat energy per unit length of track. The wire feed rate and traverse speed significantly influence the stability and geometry of the single tracks. The processing conditions at which single tracks of low wetting angle (<90◦), high aspect ratio (>1.5), high surface quality, and high hardness (close to the as-received material) can be deposited were determined. These processing conditions were considered suitable for the fabrication, surface modification and repair of functional engineering parts made of 308LSi stainless steel.

A Multi-aperture Coaxial Projector Balancing Shadow Suppression and Deblurring.

This paper proposes a projection system that optically removes the cast shadow in projection mapping. Specifically, we realize the large-aperture (LA) projection using a large-format Fresnel lens to suppress cast shadows by condensing the projection light from a wide viewing angle. However, the resolution and contrast of the projected results are significantly degraded by defocus blur, veiling glare, and stray light caused by the aberration of an LA Fresnel lens. To solve the technical problems, we employ two different approaches: optical and digital image processing methods. First, we introduce a residual projector with a typical aperture lens on the same optical axis as the LA projector, projecting the residual (i.e., high-frequency) components attenuated in the LA projection. These projectors play different roles in shadow suppression and blur compensation, both achieved by projecting simultaneously. Secondly, we optimize the pair of projection images that can balance the shadow suppression and deblurring performance of our projection system. We implemented a proof-of-concept prototype and validated the above-mentioned techniques through projection experiments and a user study.

Polarization-multiplexed zoom Moiré metalens for edge-enhanced imaging

Optical image processing with high operational efficiency has been applied as a pre-processing imaging system for image recognition. Edge-enhanced imaging as a high-efficiency optical image processing method is of great significance for feature extraction and target recognition. However, the edge-enhanced imaging system based on the 4F system and the spatial filter transforms mainly work under coherent light illumination conditions, without continuously zooming to track the spatial position of the target. Here, we demonstrate a polarization-multiplexed zoom Moiré metalens for edge-enhanced imaging under incoherent light illumination. Metalens is designed to generate polarization-dependent optical transfer functions that produce edge-enhanced images with a resolution of 1.2 µm by digital subtraction. Furthermore, continuous zoom at the range of 1-2× is realized by constructing a Moiré metalens composed of cascaded metasurfaces. The cascaded metasurfaces consist of two center-aligned dielectric metasurfaces, each with a Moiré phase sensitive to the rotation angle. By rotating the metasurface, the phase profile of the cascaded metasurfaces changes, and the effect of continuous zoom is realized. The focal length can be actively changed from 38 µm to 77 µm with the focusing efficiency of 50.3%. This metalens can be applied to machine vision, microscopic imaging, and promotes the development of multi-functional integrated optical systems.

Broadband and parallel multiple-order optical spatial differentiation enabled by Bessel vortex modulated metalens

Optical analog image processing technology is expected to provide an effective solution for high-throughput and real-time data processing with low power consumption. In various operations, optical spatial differential operations are essential in edge extraction, data compression, and feature classification. Unfortunately, existing methods can only perform low-order or selectively perform a particular high-order differential operation. Here, we propose and experimentally demonstrate a Bessel vortex modulated metalens composed of a single complex amplitude metasurface, which can perform multiple-order radial differential operations over a wide band by presetting the order of the corresponding Bessel vortex. This architecture further enables angle multiplexing to create multiple information channels that synchronously perform multi-order spatial differential operations, indicating the superiority of the proposed devices in parallel processing. Our approach may find various applications in artificial intelligence, machine vision, autonomous driving, and advanced biomedical imaging.

Multi task deep learning phase unwrapping method based on semantic segmentation

Abstract Phase unwrapping is a key step to obtain continuous phase distribution in optical phase measurement. When the wrapped phase obtained from the interference pattern is unclear and noisy, estimating the unwrapped phase becomes more challenging. As deep learning advances in optical image processing, it will enhance processing efficiency and accuracy, bringing broader possibilities for various applications. This paper introduces an innovative phase unwrapping method based on multi-task learning, aiming to simultaneously enhancing denoised images and predicting wrap count. The proposed network, named ICER-Net, comprises an encoder and two decoders, transforming the input low-luminance, noisy wrapped phase into two intermediate outputs: enhanced wrapped phase and wrap count. Finally, these two intermediate results are fused to obtain the unwrapped phase. Experimental results demonstrate that ICER-Net not only enhances the accuracy of phase unwrapping, particularly when facing challenges of various noise levels and luminance sizes but also exhibits outstanding performance in actual collected speckle phase images. This indicates that ICER-Net holds significant superiority in addressing complex issues in optical image processing.

Concurrent Image Differentiation and Integration Processings Enabled By Polarization‐Multiplexed Metasurface

AbstractOptical computing and image processing performed by sensor front‐end metasurfaces is receiving increasing interest because of advantages such as significant reduction of latency time, energy consumption, and system complexity. Despite the rapid progress, concurrent processing, the most important feature of electronic computing, has not yet been well implemented in optical computing. Here, a metasurface‐based optical image processor that can perform optical differentiation and integration tasks simultaneously is proposed. This optical front‐end processor integrates two coherent transfer functions corresponding to differential and integral convolution kernels into a built‐in metasurface by polarization encoding, allowing concurrent processing of multiple all‐optical computational tasks. The simultaneous differentiation and integration operations on images for edge enhancement and denoising are demonstrated at multiple visible wavelengths. This concurrent processing architecture paves a promising pathway toward multifunctional and higher‐speed image processing for machine vision and biomedical imaging and shows the potential to expedite and potentially supplant certain digital neural network algorithms.

Optical Image Processing using Eulerian Amplification

Recent advancements in digital photography, particularly through mobile phone cameras, microscopes, and satellite imaging, have made high-resolution image capture increasingly accessible. However, these captured images often contain subtle changes in color resolution and density that are challenging to observe with the naked eye. This study explores the application of Eulerian amplification as a method to detect and enhance such small-amplitude variations, making them visible for improved analysis. Unlike traditional approaches, Eulerian methods are particularly effective for processing smooth structures and high-quality printed materials where minor amplifications in color can reveal significant grading details. Using a pixel-by-pixel Eulerian path analysis, this method allows for a detailed comparison of color intensity values on a fine 8-bit scale, which ranges from 0 to 255. Through this analysis, color distribution patterns are visualized across the spatial structure of the image, which is particularly beneficial for identifying specific variations in quality in printed materials, such as highly graded cards. To manage large image structures, spatial decomposition using a multi-level Gaussian pyramid technique is employed. By applying temporal filtering within select frequency bands (0.4–4 Hz) at a coarse pyramid level, the algorithm effectively pools spatial data, enabling the detection of color density shifts that may indicate image defects. The amplified image data is further processed using a classifier neural network, which provides high sensitivity and specificity in detecting edge defects, image centering issues, scuffs, and breakages. The system demonstrates promising results in identifying true positives while maintaining low false-negative rates, thus confirming the reliability of deep machine learning algorithms in high-sensitivity defect detection. This paper presents the significance of Eulerian amplification in optical image processing, offering a robust tool for precise, automated defect detection across a variety of high-resolution image types.

Retraction Note: Application of computer simulation based on optical image processing in monitoring metabolic regulation data during exercise training

Retraction Note: Application of computer simulation based on optical image processing in monitoring metabolic regulation data during exercise training

Damage Scene Change Detection Based on Infrared Polarization Imaging and Fast-PCANet

Change detection based on optical image processing plays a crucial role in the field of damage assessment. Although existing damage scene change detection methods have achieved some good results, they are faced with challenges, such as low accuracy and slow speed in optical image change detection. To solve these problems, an image change detection approach that combines infrared polarization imaging with a fast principal component analysis network (Fast-PCANet) is proposed. Firstly, the acquired infrared polarization images are analyzed, and pixel image blocks are extracted and filtered to obtain the candidate change points. Then, the Fast-PCANet network framework is established, and the candidate pixel image blocks are sent to the network to detect the change pixel points. Finally, the false-detection points predicted by the Fast-PCANet are further corrected by region filling and filtering to obtain the final binary change map of the damage scene. Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. The experimental results show that the proposed Fast-PCANet method improves the PCC and the Kappa coefficient of infrared polarization images over infrared intensity images by 6.77% and 13.67%, respectively. Meanwhile, the inference speed can be more than seven times faster. The results verify that the proposed approach is effective and efficient for the change detection task with infrared polarization imaging. The study can be applied to the damage assessment field and has great potential for object recognition, material classification, and polarization remote sensing.

Far-field sub-diffraction focusing and controlled focus shaping of circularly polarized light using a dielectric phase plate

In recent years, sub-diffraction focusing has received substantial attention due to its versatility. However, achieving a flexible sub-diffraction focusing in the far field remains stimulating. Existing techniques either require complex fabrication facilities or are limited to the short focal length and high numerical aperture (NA) of the imaging system. Here, we introduce an optimization method for sub-diffraction focusing of a circularly polarized beam in the far field with a lens of large focal length. A cost-effective dielectric phase plate serves the purpose. By employing a phase plate composed of a thin layer of dielectric Si3N4, the phase of the propagating beam is modulated in the beam’s cross-section, which is divided into two regions of the opposite phase by the plate. A sub-diffraction focusing is achieved for a proper tunning between the two regions. In addition to sub-diffraction focusing, the phase plate is also capable of shaping the focus into a doughnut-shaped and a flat-top profile in the far field. This design provides a simple solution for sub-diffraction focusing and focus shaping that will find potential applications in optical imaging, optical trapping, and material processing.

A Method for Recognition and Coordinate Reference of Autonomous Underwater Vehicles to Inspected Objects of Industrial Subsea Structures Using Stereo Images

To date, the development of unmanned technologies using autonomous underwater vehicles (AUVs) has become an urgent demand for solving the problem of inspecting industrial subsea structures. A key issue here is the precise localization of AUVs relative to underwater objects. However, the impossibility of using GPS and the presence of various interferences associated with the dynamics of the underwater environment do not allow high-precision navigation based solely on a standard suite of AUV navigation tools (sonars, etc.). An alternative technology involves the processing of optical images that, at short distances, can provide higher accuracy of AUV navigation compared to the technology of acoustic measurement processing. Although there have been results in this direction, further development of methods for extracting spatial information about objects from images recorded by a camera is necessary in the task of calculating the exact mutual position of the AUV and the object. In this study, in the context of the problem of subsea production system inspection, we propose a technology to recognize underwater objects and provide coordinate references to the AUV based on stereo-image processing. Its distinctive features are the use of a non-standard technique to generate a geometric model of an object from its views (foreshortening) taken from positions of a pre-made overview trajectory, the use of various characteristic geometric elements when recognizing objects, and the original algorithms for comparing visual data of the inspection trajectory with an a priori model of the object. The results of experiments on virtual scenes and with real data showed the effectiveness of the proposed technology.

Post-Surgical Non-Invasive Wound Healing Monitoring in Oropharyngeal Mucosa.

Postoperative bleeding is the most significant complication of tonsillectomy. Regular monitoring of post-surgical wound healing in the pharynx is required. For this purpose, we propose endoscope-based non-invasive perfusion mapping and quantification. The combination of imaging photoplethysmography and image processing provides automated wound area selection and microcirculation characterization. In this feasibility study, we demonstrate the first results of the proposed approach to wound monitoring in clinical trial on eight patients after tonsillectomy. Combination of probe-based optical system and image processing algorithms can provide the valuable and consistent data on perfusion distribution. The quantitative microcirculation data obtained 1, 4, and 7 days after surgery are in good agreement with existing monitoring protocols.

Carbon fibre detection in polymer composites reinforced by chopped carbon fibres through digital image processing and machine learning

Mechanical and thermodynamical properties and thus machinability of carbon fibre reinforced polymer composites significantly depend on the fibre orientation relative to the load direction. However, the orientations of the fibre groups in polymer composites reinforced by chopped carbon fibres are stochastic; therefore, the properties and machinability of such composites are challenging to plan, predict and optimise. We developed four different and novel approaches for fibre detection in polymer composites reinforced by chopped carbon fibres: (i) detecting the fibres through naked eye supported manual drawing, (ii) digital image processing of optical images, (iii) machine learning-based fibre detection, and (iv) rectangle fitting on the outputs of the automated processes using the Chaudhuri and Samal method. The applicability of the novel approaches was tested through optically captured images of polymer composites reinforced by chopped carbon fibres. The developed methods are each capable of detecting fibre groups at the top and bottom of the composite plate with certain limitations. The rectangle fitting approaches performed the best from the point of view of correctly identifying of fibre groups, followed by the machine learning-based and the conventional digital image processed, respectively. As a result of this study, the machining process planning and condition monitoring of polymer composites reinforced by chopped carbon fibres is more deeply supported.

Sensor-Enhanced Smart Gripper Development for Automated Meat Processing.

Grasping and object manipulation have been considered key domains of Cyber-Physical Systems (CPS) since the beginning of automation, as they are the most common interactions between systems, or a system and its environment. As the demand for automation is spreading to increasingly complex fields of industry, smart tools with sensors and internal decision-making become necessities. CPS, such as robots and smart autonomous machinery, have been introduced in the meat industry in recent decades; however, the natural diversity of animals, potential anatomical disorders and soft, slippery animal tissues require the use of a wide range of sensors, software and intelligent tools. This paper presents the development of a smart robotic gripper for deployment in the meat industry. A comprehensive review of the available robotic grippers employed in the sector is presented along with the relevant recent research projects. Based on the identified needs, a new mechatronic design and early development process of the smart gripper is described. The integrated force sensing method based on strain measurement and magnetic encoders is described, including the adjacent laboratory and on-site tests. Furthermore, a combined slip detection system is presented, which relies on an optical flow-based image processing algorithm using the video feed of a built-in endoscopic camera. Basic user tests and application assessments are presented.

Optical Imaging Model Based on GPU-Accelerated Monte Carlo Simulation for Deep-Sea Luminescent Objects

Modeling and simulating the underwater optical imaging process can assist in optimizing the configuration of underwater optical imaging technology. Based on the Monte Carlo (MC) method, we propose an optical imaging model which is tailored for deep-sea luminescent objects. Employing GPU parallel acceleration expedites the speed of MC simulation and ray-tracing, achieving a three-order-of-magnitude speedup over a CPU-based program. A deep-sea single-lens imaging system is constructed in the model, composed of a luminescent object, water medium, double-convex lens, aperture diaphragm, and sensor. The image of the luminescent object passing through the imaging system is generated using the forward ray-tracing method. This model enables an intuitive analysis of the inherent optical properties of water and imaging device parameters, such as sensor size, lens focal length, field of view (FOV), and camera position on imaging outcomes in the deep-sea environment.

Fractional fourier transform and its application

The Fourier Transform (FT) is a linear transformation for the primitive function. It takes some set of functions to be an orthogonal basis. Its physical meaning is to transfer the primitive function onto each set of base functions. Because it can convert functions between the time and frequency domains, the FT is widely employed in many fields. The Fractional Fourier Transform (FrFT) is an improvement and progress based on the FT. This paper will define the FT and FrFT. Then the distinction between FrFT and FT is discussed. Finally, specific examples of its application in processing digital image are provided. FrFT is the process of transforming an image function into a series of periodic functions. The FrFT is used as a powerful mathematical tool to understand non- smooth signals, nonlinear systems and complicated phenomena, which is significant and has broad possibilities in the fields of signal processing, communication, image processing, optical imaging and quantum information processing.

Two-dimensional optical multiple-order differentiations based on spatial spectrum modulation.

Optical differential operations have recently attracted considerable attention owing to the capabilities of ultrafast speed and low power consumption. The transfer function, which embodies the frequency-domain characteristics of differential systems, plays an important role in differentiator design. Here, we report a super-Gaussian aperture differential filter, and we reveal unique characteristics of odd- and even-order transfer functions and corresponding differential effects via spatial spectrum modulation. We show that the feature of the transfer function is well maintained, and more precise differentiation can be achieved using the designed filter. Two-dimensional first- to fifth-order full and partial differentiations are implemented both theoretically and experimentally. Our work provides an approach for engineering customized multiple-order differentiators and promotes the advancements of related areas such as optical analog computing and image processing.

Parallel photonic chip for nanosecond end-to-end image processing, transmission, and reconstruction

Image processing, transmission, and reconstruction constitute a major proportion of information technology. The rapid expansion of ubiquitous edge devices and data centers has led to substantial demands on the bandwidth and efficiency of image processing, transmission, and reconstruction. The frequent conversion of serial signals between the optical and electrical domains, coupled with the gradual saturation of electronic processors, has become the bottleneck of end-to-end machine vision. Here, we present an optical parallel computational array chip (OPCA chip) for end-to-end processing, transmission, and reconstruction of optical intensity images. By proposing constructive and destructive computing modes on the large-bandwidth resonant optical channels, a parallel computational model is constructed to implement end-to-end optical neural network computing. The OPCA chip features a measured response time of 6 ns and an optical bandwidth of at least 160 nm. Optical image processing can be efficiently executed with minimal energy consumption and latency, liberated from the need for frequent optical–electronic and analog–digital conversions. The proposed optical computational sensor opens the door to extremely high-speed processing, transmission, and reconstruction of visible contents with nanoseconds response time and terahertz bandwidth.

Quantitative skin surface hydration measurement by visible optical image processing: A pilot study.

Skin barrier function is significantly impacted by skin moisture. Most non-invasive evaluation techniques to measure skin surface hydration relying on its electrical properties, which are limited in scope and have unstable operations. Applying image processing for skin hydration assessment is uncommon, with an emphasis on skin-capacitive pictures and near-infrared images in general, which demand a certain spectrum. As a result, there is an increasing need for wide-area skin hydration evaluation and mapping. Thestudy aimsto propose a quantitative evaluation algorithmforskin surface hydrationfrom visible-light images. Threedeviceswere applied tomeasureskin hydration:skin image capturedevice and tworecognizedcommercialskindevices.A digital image processing system creates a new index, called GVR, to symbolize skin surface moisture. TheCLAHE algorithmwas appliedto enhance the contrast of skin image, andafter calculating it with the monochrome image,the skin reflectance imagewas segmented.The GVR was estimated using the values of the individual sites and the entire skin. The correlation coefficient between the three methods was examined using statistical analysis to assess the performance of GVR. Skin hydrationestimated from visible-light imagesis influenced by the entire facial structure in addition to specific areas.The electrical and visible image evaluations showed a strong association with a significant difference. It was discovered that reflecting measures from visible images provide a quick and efficient way to quantify the moisture of the skin's surface.

Time-gated imaging through dense fog via physics-driven Swin transformer

Imaging through the fog is valuable for many areas, such as autonomous driving and cosmic exploration. However, due to the influence of strong backscattering and diffuse reflection generated by the dense fog on the temporal-spatial correlations of photons returning from the target object, the reconstruction quality of most existing methods is significantly reduced under dense fog conditions. In this study, we describe the optical scatter imaging process and propose a physics-driven Swin Transformer method utilizing Time-of-Flight (ToF) and Deep Learning principles to mitigate scattering effects and reconstruct targets in conditions of heterogeneous dense fog. The results suggest that, despite the exponential decrease in the number of ballistic photons as the optical thickness of fog increases, the Physics-Driven Swin Transformer method demonstrates satisfactory performance in imaging targets obscured by dense fog. Importantly, this article highlights that even in dense fog imaging experiments with optical thickness reaching up to 3.0, which exceeds previous studies, commonly utilized quantitative evaluation metrics like PSNR and SSIM indicate that our method is cutting-edge in imaging through dense fog.