Parallel Image Processing Research Articles

Digital Image Processing.

Digital image processing is the use of a digital computer to process a digital image using an algorithm. As a subcategory or area of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as noise accumulation and distortion during processing. This review article provides a comprehensive literature review of various image processing techniques along with a brief introduction to digital image processing that defines its scope and importance, thereby highlighting the importance of its use in the field of electronic engineering applications that are used in today's commercial and industrial scenarios. It takes into consideration the research work done by various leading scholars in the field of electronic engineering and also the description of various software tools like MATLAB which is used for the practical implementation of the given problem lying in its domain.

Single-layer spatial analog meta-processor for imaging processing

Computational meta-optics brings a twist on the accelerating hardware with the benefits of ultrafast speed, ultra-low power consumption, and parallel information processing in versatile applications. Recent advent of metasurfaces have enabled the full manipulation of electromagnetic waves within subwavelength scales, promising the multifunctional, high-throughput, compact and flat optical processors. In this trend, metasurfaces with nonlocality or multi-layer structures are proposed to perform analog optical computations based on Green’s function or Fourier transform, intrinsically constrained by limited operations or large footprints/volume. Here, we showcase a Fourier-based metaprocessor to impart customized highly flexible transfer functions for analog computing upon our single-layer Huygens’ metasurface. Basic mathematical operations, including differentiation and cross-correlation, are performed by directly modulating complex wavefronts in spatial Fourier domain, facilitating edge detection and pattern recognition of various image processing. Our work substantiates an ultracompact and powerful kernel processor, which could find important applications for optical analog computing and image processing.

Computing metasurfaces for all-optical image processing: a brief review

Abstract Computing metasurfaces are two-dimensional artificial nanostructures capable of performing mathematical operations on the input electromagnetic field, including its amplitude, phase, polarization, and frequency distributions. Rapid progress in the development of computing metasurfaces provide exceptional abilities for all-optical image processing, including the edge-enhanced imaging, which opens a broad range of novel and superior applications for real-time pattern recognition. In this paper, we review recent progress in the emerging field of computing metasurfaces for all-optical image processing, focusing on innovative and promising applications in optical analog operations, image processing, microscopy imaging, and quantum imaging.

An application-specific image processing array based on WSe2 transistors with electrically switchable logic functions

With the rapid development of artificial intelligence, parallel image processing is becoming an increasingly important ability of computing hardware. To meet the requirements of various image processing tasks, the basic pixel processing unit contains multiple functional logic gates and a multiplexer, which leads to notable circuit redundancy. The pixel processing unit retains a large optimizing space to solve the area redundancy issues in parallel computing. Here, we demonstrate a pixel processing unit based on a single WSe2 transistor that has multiple logic functions (AND and XNOR) that are electrically switchable. We further integrate these pixel processing units into a low transistor-consumption image processing array, where both image intersection and image comparison tasks can be performed. Owing to the same image processing power, the consumption of transistors in our image processing unit is less than 16% of traditional circuits.

Sensing approach for the in-situ determination of spatter motion within PBF-LB/M

Within powder bed fusion of metals by a laser-beam (PBF-LB/M) spatter particles evolve due to the entrainment of powder particles and their ejection along the vapor plume. Motion properties of the evolving spatter particles are directly linked to the process zone topography, qualifying them as an interesting process signature for in-situ sensing. This work shows an industrially applicable sensing approach which allows the determination of three-dimensional spatter positions in real-time. A camera setup is derived reducing ambiguity within the reconstruction at high framerates. The presented parallel image processing and reconstruction algorithm allows for the determination of three-dimensional spatter coordinates in real-time.

Magnetic resonance brain volume property-based accelerate medical image algorithms using graphics processing unit

This paper elaborates the design and implementation details of parallel image processing techniques that are used to accelerate the medical image algorithms with compute unified device architecture (CUDA) supported graphics processing unit (GPU). The algorithms are chosen from denoising, morphology, clustering, and segmentation. Three parallel computing models are developed based on the properties of algorithms and magnetic resonance imaging (MRI) principles. The acceleration of parallel algorithms is compared with that of sequential central processing unit (CPU) implementation measured in terms of speedup folds (×).

Switchable and simultaneous spatiotemporal analog computing with computational graphene-based multilayers

In the past few years, analog computing has experienced rapid development but mostly for a single function. Motivated by parallel space-time computing and miniaturization, we show that reconfigurable graphene-based multilayers offer a promising path towards spatiotemporal computing with integrated functionalities by properly engineering both spatial- and temporal-frequency responses. This paper employs a tunable graphene-based multilayer to enable analog signal and image processing in both space and time by tuning the external bias. In the first part of the paper, we propose a switchable analog computing paradigm in which the proposed multilayer can switch among defined performances by selecting a proper external voltage for graphene monolayers. Spatial isotropic differentiation and edge detection in the spatial channel and first-order temporal differentiation and multilayer-based phaser with linear group-delay response in the temporal channel are demonstrated. In the second section of the paper, simultaneous and parallel spatiotemporal analog computing is demonstrated. The proposed multilayer processor has almost no static power consumption due to its floating-gate configuration. The spatial- and temporal-frequency transfer functions (TFs) are engineered using a transmission line (TL) model, and the obtained results are validated with full-wave simulations. Our proposal will enable real-time parallel spatiotemporal analog signal and image processing.

Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique

Image thinning is the most essential pre-processing technique that plays major role in image processing applications such as image analysis and pattern recognition. It is a process that reduces a thick binary image into thin skeleton. In the present paper we have used hybrid parallel thinning algorithm to obtain the skeleton of the binary image. The result skeleton contains one pixel width which preserves the topological properties and retains the connectivity.

Parallel Processing Algorithms of Ultrasound Images

With the rapid development of microelectronics technology, digital image processing technology has deepened our lives. In the past, image processing mostly used software processing. Due to the large number of processing algorithms and the complexity of the algorithm, it could not meet the real-time requirements. Therefore, this paper proposes a method for parallel processing of ultrasound images based on fax by using a programmable logic device. Image fusion is to extract and synthesize complementary information from different sensors to obtain a more accurate, comprehensive and reliable description of the target or scene. The transmission of images and other images is achieved by the sound of various sensors, which is far from enough. This article presents the use of hardware description language to design image processing and drivers. The core of the print head, scan head and stepper motor can use the control interface to perform selective and parameter variable median filtering on the scanned graphic image through software. Edge sharpening, image field separation, binarization and other hardware processing are compared with a series of processing directly using software, and the optimal processing plan can be made well, and the processed image can be displayed and printed out on the. This design can produce practical results for designs in the fields of biomedicine, sports, and energy production.

Laplace metasurfaces for optical analog computing based on quasi-bound states in the continuum

Laplace operation, the isotropic second-order differentiation, on spatial functions is an essential mathematical calculation in most physical equations and signal processing. Realizing the Laplace operation in a manner of optical analog computing has recently attracted attention, but a compact device with a high spatial resolution is still elusive. Here, we introduce a Laplace metasurface that can perform the Laplace operation for incident light-field patterns. By exciting the quasi-bound state in the continuum, an optical transfer function for nearly perfect isotropic second-order differentiation has been obtained with a spatial resolution of wavelength scale. Such a Laplace metasurface has been numerically validated with both 1D and 2D spatial functions, and the results agree well with that of the ideal Laplace operation. In addition, the edge detection of a concerned object in an image has been demonstrated with the Laplace metasurface. Our results pave the way to the applications of metasurfaces in optical analog computing and image processing.

Implementation of Thermal Event Image Processing Algorithms on NVIDIA Tegra Jetson TX2 Embedded System-on-a-Chip

Advances in Infrared (IR) cameras, as well as hardware computational capabilities, contributed towards qualifying vision systems as reliable plasma diagnostics for nuclear fusion experiments. Robust autonomous machine protection and plasma control during operation require real-time processing that might be facilitated by Graphics Processing Units (GPUs). One of the current aims of image plasma diagnostics involves thermal events detection and analysis with thermal imaging. The paper investigates the suitability of the NVIDIA Jetson TX2 Tegra-based embedded platform for real-time thermal events detection. Development of real-time processing algorithms on an embedded System-on-a-Chip (SoC) requires additional effort due to the constrained resources, yet low-power consumption enables embedded GPUs to be applied in MicroTCA.4 computing architecture that is prevalent in nuclear fusion projects. For this purpose, the authors have proposed, developed and optimised GPU-accelerated algorithms with the use of available software tools for NVIDIA Tegra systems. Furthermore, the implemented algorithms are evaluated and benchmarked on Wendelstein 7-X (W7-X) stellarator experimental data against the corresponding alternative Central Processing Unit (CPU) implementations. Considerable improvement is observed for the accelerated algorithms that enable real-time detection on the embedded SoC platform, yet some encountered limitations when developing parallel image processing routines are described and signified.

Computational Benefits, Limitations and Techniques of Parallel Image Processing

The main purpose of digitalized image processing is to enhance the quality of images and subsequently to facilitate the process of feature classification and extraction. This process is effectively utilized in healthcare imaging, computer visioning, astronomy, meteorology, and remote sensing among other essential fields. The main issue is that this technique takes a lot of time even though it provides convenient and efficient means of addressing the prevailing issue. In this paper, we shall provide a comparative evaluation of the present contributions adding to the application of parallel image processing including their limitations and benefits. Another key segment of research in this paper is to evaluate parallel computing, presently available techniques, tools and architecture in different image processing applications. As such, this paper is purposed to critically evaluate the role of parallel imaging in the field of healthcare imaging.

Reciprocal Metasurfaces for On-Axis Reflective Optical Computing

Analog computing has emerged as a promising candidate for real-time and parallel continuous data processing. This paper presents a reciprocal way for realizing asymmetric optical transfer functions (OTFs) in the reflection side of the on-axis processing channels. It is rigorously demonstrated that the presence of Cross-polarization Exciting Normal Polarizabilities (CPENP) of a reciprocal metasurface circumvents the famous challenge of Green's function approach in implementation of on-axis reflective optical signal processing while providing dual computing channels under orthogonal polarizations. Following a comprehensive theoretical discussion and as a proof of concept, an all-dielectric optical metasurface is elaborately designed to exhibit the desired surface polarizabilities, thereby reflecting the first derivative and extracting the edges of images impinging from normal direction. The proposed study offers a flexible design method for on-axis metasurface-based optical signal processing and also, dramatically facilitates the experimental setup required for ultrafast analog computation and image processing.

Low power convolutional architectures: Three operator switching systems based on forgetting memristor bridge

With the development of technology and society, artificial intelligence has entered the era of deep learning neural networks. Convolution operation and image processing are fundamental technologies for deep neural networks and artificial intelligence, so reducing the energy consumption of convolution operation and increasing the speed of image processing will promote the development of a sustainable intelligent society. Traditional image processing technologies are based on the Von Neumann architecture, which is slow and not convenient for the hardware implementation of neural networks. Therefore, this paper breaks through the Von Neumann architecture, and uses the forgetting memristor bridge to realize the parallel image processing on the neumorphic chips. We use the forgetting characteristics of the memristor bridge to switch operators, and design single-operator switching, double-operator switching and K-operator switching three system architectures. The image operator switching systems designed in this paper not only have the similar processing effect of the traditional way, but also have the advantages of easy control, fast running speed (the processing speed is reduced from ms to μs), and low power consumption (the power consumption is reduced by nearly half), which will bring immeasurable benefits to the sustainable intelligent society.

A review of dielectric optical metasurfaces for spatial differentiation and edge detection.

Dielectric metasurfaces-based planar optical spatial differentiator and edge detection have recently been proposed to play an important role in the parallel and fast image processing technology. With the development of dielectric metasurfaces of different geometries and resonance mechanisms, diverse on-chip spatial differentiators have been proposed by tailoring the dispersion characteristics of subwavelength structures. This review focuses on the basic principles and characteristic parameters of dielectric metasurfaces as first- and second-order spatial differentiators realized via the Green's function approach. The spatial bandwidth and polarization dependence are emphasized as key properties by comparing the optical transfer functions of metasurfaces for different incident wavevectors and polarizations. To present the operational capabilities of a two-dimensional spatial differentiator in image information acquisition, edge detection is described to illustrate the practicability of the device. As an application example, experimental demonstrations of edge detection for different biological cells and a flower mold are discussed, in which a spatial differentiator and objective lens or camera are integrated in three optical pathway configurations. The realization of spatial differentiators and edge detection with dielectric metasurfaces provides new opportunities for ultrafast information identification in biological imaging and machine vision.

RETRACTED: Simulation of swimming sports image recognition based on multi-core processor and dynamic image sampling

Abstract Human understanding of human movement is one of the most important areas of research activities. In recent years, the use of RGB-D movement recognition data has raised concerns. With artificial intelligence development, the depth of learning technology has made great success in computer vision. A variety of application scenarios and real-time constraints computer vision applications spurred on computer architecture conducted extensive research. These studies take advantage of image processing algorithms and computer vision functions to maximize and improve efficiency through the hardware's scalability. Growth, programmable computing power, and multi-core architecture's potential to accelerate parallel image processing and computer vision algorithms provide a broad prospect. Conduct a new study to discover the unique attributes of three computer vision algorithms that are widely used and determine the optimal computing architecture for each algorithm. Advantages of parallel processing hardware to provide modern programmable graphics data and the thread of instructions, which is significantly faster than the standard CPU implementation.

Parallel Raster Scan for Euclidean Distance Transform

Distance transform (DT) and Voronoi diagrams (VDs) have found many applications in image analysis. Euclidean distance transform (EDT) can generate forms that do not vary with the rotation, because it is radially symmetrical, which is a desirable characteristic in distance transform applications. Recently, parallel architectures have been very accessible and, particularly, GPU-based architectures are very promising due to their high performance, low power consumption and affordable prices. In this paper, a new parallel algorithm is proposed for the computation of a Euclidean distance map and Voronoi diagram of a binary image that mixes CUDA multi-thread parallel image processing with a raster propagation of distance information over small fragments of the image. The basic idea is to exploit the throughput and the latency in each level of memory in the NVIDIA GPU; the image is set in the global memory, and can be accessed via texture memory, and we divide the problem into blocks of threads. For each block we copy a portion of the image and each thread applies a raster scan-based algorithm to a tile of m×m pixels. Experiment results exhibit that our proposed GPU algorithm can improve the efficiency of the Euclidean distance transform in most cases, obtaining speedup factors that even reach 3.193.

Distributed ImageJ(Fiji): a framework for parallel image processing

ImageJ is an open-source application widely used for image processing. It has developer API that can be used to implement new plugins for specific image processing tasks. However, ImageJ wasn't designed to work on distributed systems. Currently, it is still being used on single machines to process large medical images, which takes several hours to complete. In this article, we present the approaches to make several essential and widely used ImageJ plugins to work in a cluster. As the cluster nodes parallelly run the existing plugins for image processing, they write the results on a shared drive. But one of the main challenges is, merging those results with high accuracy. Several ImageJ plugins were developed to distribute tasks and generate combined results efficiently. The existing 3D-Object-Counter plugin was used for testing the designed system. The experimental results on the test images of 3D objects stored in Tagged Image File Format(TIFF) show faster processing time with high accuracy and similarity compared to the single machine-based results. The image processing time depends on the number of nodes in the cluster. So, we present a mathematical model that determines the cluster size automatically for optimizing the overall image processing time.

Landslide Recognition by Deep Convolutional Neural Network and Change Detection

It is a technological challenge to recognize landslides from remotely sensed (RS) images automatically and at high speeds, which is fundamentally important for preventing and controlling natural landslide hazards. Many methods have been developed, but there remains room for improvement for stable, higher accuracy, and high-speed landslide recognition for large areas with complex land cover. In this article, a novel integrated approach combining a deep convolutional neural network (CNN) and change detection is proposed for landslide recognition from RS images. Logically, it comprises the following four parts. First, a CNN for landslide recognition is built based on training data sets from RS images with historical landslides. Second, the object-oriented change detection CNN (CDCNN) with a fully connected conditional random field (CRF) is implemented based on the trained CNN. Third, the preliminary CDCNN is optimized by the proposed postprocessing methods. Finally, the results are further enhanced by a set of information extraction methods, including trail extraction, source point extraction, and attribute extraction. Furthermore, in the implementation of the proposed approach, image block processing and parallel processing strategies are adopted. As a result, the speed has been improved significantly, which is extremely important for RS images covering large areas. The effectiveness of the proposed approach has been examined using two landslide-prone sites, Lantau Island and Sharp Peak, Hong Kong, with a total area of more than 70 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Besides its high speed, the proposed approach has an accuracy exceeding 80%, and the experiments demonstrate its high practicability.

Computational Benefits, Limitations and Techniques of Parallel Image Processing

The main purpose of digitalized image processing is to enhance the quality of images and subsequently to facilitate the process of feature classification and extraction. This process is effectively utilized in healthcare imaging, computer visioning, astronomy, meteorology, and remote sensing among other essential fields. The main issue is that this technique takes a lot of time even though it provides convenient and efficient means of addressing the prevailing issue. In this paper, we shall provide a comparative evaluation of the present contributions adding to the application of parallel image processing including their limitations and benefits. Another key segment of research in this paper is to evaluate parallel computing, presently available techniques, tools and architecture in different image processing applications. As such, this paper is purposed to critically evaluate the role of parallel imaging in the field of healthcare imaging.