ACCELERATING IMAGE PROCESSING USING PARALLEL COMPUTATIONS IN OPENMP: DEVELOPMENT AND PERFORMANCE ANALYSIS OF A GRAPHICS EDITOR

Massively deployed inside Sony PS3 platforms, the STI Cell Broadband Engine is a multi-core processor with a PowerPC host processor (PPE) and 8 synergic processor engines (SPEs). In this paper, we describe three image processing applications which we implemented on the Cell BE. We report the performance measured on one Cell blade with varying numbers of synergic processor engines enabled, and with varying application parameters. These results were compared to the results obtained on the Cell BE running a single PPE and with all 8 SPEs disabled. The results reveal the superior performance of image processing and SIMD-prone applications on the 9-core Cell BE processor over conventional RISC processors.

The first quarter of 2007 started as an active season for the real-time image processing community. The annual SPIE Conference on Real-Time Image Processing, which was held in San Jose, CA, highlighted recent developments in this growing branch of image processing. As one of the few regular events with its focus matching that of JRTIP, one witnessed the growth in applications of real-time image and video processing. The conference served as an effective instrument to foster the dissemination of recent results and to leverage the cohesion of the real time image processing community. The preparations for the next SPIE Conference on Real-Time Image Processing in 2008 are already progressing and its call-for-papers is included at the end of this issue. In 2007, JRTIP is going to seek involvement of more scientists in the field of real-time image and video processing. After 1 year of operation, the editors-in-chief have started to recruit additional associate editors to enlarge the expertise base of the editorial board, to solicit manuscript submissions from potential authors, and to make the reviewing process more efficient. Interested colleagues should contact the editors-in-chief expressing their Associate Editor candidature in support of one or more fields within the focus of JRTIP. In addition, they wish to encourage researchers to act as guest editors for special issues on any real-time image or video processing related topics of interest to them. It is possible to schedule two special issues in 2007, provided that a draft call-for-papers is sent to the editors-in-chief as soon as possible. The third issue of JRTIP presents four articles, two are survey papers and two are original research papers. These articles address different real-time image processing perspectives ranging from algorithmic to hardware issues. The first survey paper deals with the real-time filtering in 2D and 3D using ordered statistics that are supported by a rich set of experimental results derived from several algorithmic variations. Performance measurements are used to demonstrate the effect of noise suppression and preservation of fine image structures and to show the DSP implementation of denoising filters. The second survey paper introduces a non-invasive analysis of dynamic processes in living cells using microscopic computer vision techniques for understanding the interactions between cellular and molecular functional units and for providing novel insights into the cell biology. The third article is an original research paper covering a machine vision application involving vision-based position sensing for aerial refueling of unmanned aerial vehicles in real-time. A full image processing chain is investigated and tested in a MATLAB/Simulink soft real-time environment as well as in a Linux/RTAI hard real-time environment. The feasibility of the real-time implementation is shown using off-the-shelf commercial hardware. The fourth article is another original research paper describing a hardware architecture for real-time fractal coding for image compression. The introduced parallel hardware architecture contains units for domain M. F. Carlsohn (&) Ingenieurberatung fur Computer Vision und Bildkommunikation, Am Heiddamm 36g, 28355 Bremen, Germany e-mail: matthias.carlsohn@t-online.de

. The development of an Image Processing (IP) application is a complex activity, which can be greatly alleviated by user-friendly graphical programming environments. Our major objective is to help IP experts reuse parts of their applications. A first work towards knowledge reuse has been to propose a suitable representation of the strategies of IP experts by means of IP plans (trees of tasks, methods and tools). This paper describes the CBR module of our interactive system for the development of IP plans. After a brief presentation of the overall architecture of the system and its other modules, we explain the distinction between an IP case and an IP plan, and give the selection criteria and functions that are used for similarity calculation. The core of the CBR module is a search/adaptation algorithm, whose main steps are detailed: retrieval of suitable cases, recursive adaptation of the selected one and memorization of new cases. The system’s implementation is presently completed; its functioning is described in a session showing the kind of assistance provided by the CBR module during the development of a new IP application.

This article reviews the various image processing techniques in MATLAB and also hardware implementation in FPGA using Xilinx system generator. Image processing can be termed as processing of images using mathematical operations by using various forms of signal processing techniques. The main aim of image processing is to extract important features from an image data and process it in a desired manner and to visually enhance or to statistically evaluate the desired aspect of the image. This article provides an insight into the various approaches of Digital Image processing techniques in Matlab. This article also provides an introduction to FPGA and also a step by step tutorial in handling Xilinx System Generator. The Xilinx System Generator tool is a new application in image processing and offers a friendly environment design for the processing. This tool support software simulation, but the most important is that can synthesize in FPGAs hardware, with the parallelism, robust and speed, this features are essentials in image processing. Implementation of these algorithms on a FPGA is having advantage of using large memory and embedded multipliers. Advances in FPGA technology with the development of sophisticated and efficient tools for modelling, simulation and synthesis have made FPGA a highly useful platform.

The aim of digital image processing is to improve the quality of image and subsequently to perform features extraction and classification. It is effectively used in computer vision, medical imaging, meteorology, astronomy, remote sensing and other related field. The main problem is that it is generally time consuming process; Parallel Computing provides an efficient and convenient way to address this issue. Main purpose of this review is to provide the comparative study of the existing contributions of implementing parallel image processing applications with their benefits and limitations. Another important aspect of this study is to provide the brief introduction of parallel computing and currently available parallel architecture, tools and techniques used for implementing parallel image processing. The aim is to discuss the problems encountered to implement parallel computing in various image processing applications. In this research we also tried to describe the role of parallel image processing in the field of medical imaging.

This Human Skin Colour detection is analyzed to be a most suitable and strong cue for face detection and face recognition which finds various application in domain like surveillance, face biometrics, gesture recognition, interactive game application and many other. These applications require fast image processing in real time and demands enormously high performance with respect to time and processing speed. Earlier work done on sequential architecture does not provide required capability and can be achieved by parallel programming. As Image processing applications shows high degree of parallelism, they prove excellent source for multi-core platform. We can use GPU multithreaded parallel computing techniques to improve the speed of image processing. The paper proposes a data parallelism programming model for Human Skin Colour Detection algorithm. The objective is to increase the computational speed of the algorithm through data parallelism using CUDA framework. The framework is supported by OpenCV libraries and implemented by GPU (Graphics Processing Unit). The evaluation is done on basis of comparative analysis of serial and parallel programming computing. The pixel based Explicitly Defined Region skin classifier is used and RGB colour space is chosen due to its wider use in storing and processing of digital image data. To gain substantial acceleration in image computations, skin classifier code is off-loaded to GPU having compute capacity of 3.5 and rest of code is executed on CPU. We have tested module on static images, as well as on live camera captured images. SFA standard dataset is utilized to evaluate performance. Speedup of 23.45% was achieved using parallel programming.

The rapid development of artificial intelligence (AI) has led to significant transformations in the field of image processing and digital photography. AI-based systems enable automated image enhancement, classification, restoration, and content analysis, greatly improving both technical efficiency and creative possibilities. This paper presents a conceptual model for the integration of artificial intelligence methods in photographic image processing. The study examines key preprocessing techniques, major application areas, commonly used AI tools, and the ethical challenges associated with AI-driven image manipulation. By analyzing current research and technological trends, the paper demonstrates that AI significantly enhances image quality and processing speed while simultaneously raising important concerns related to data privacy, transparency, and authenticity. The proposed conceptual model aims to provide a structured framework for understanding how artificial intelligence can be effectively and responsibly applied in photography.

With increasing applications of image processing in manufacturing, it has become necessary to provide industry with powerful as well as flexible means of adapting the technology to the solution of different kinds of tasks at hand. Besides the standard components of an image processing system, the availability ofvarious software tools to fill the gap between design, digitization, measurement and the manufacturing phases should be emphasized. The users could e.g., desire to transfer grabbed images into certain computer graphics data format for further processing. They may want to apply image processing for the measurement of machine parts interactively or automatically, or they may want to convert the pictures processd, e.g. with edge detectors, into some CAD data format, or even to generate a numerical control file to machine the parts directly from the image processing data. With this motivation, two software packages FIPS—Flexible Image Processing System, and IQIS— Integrated QUality Inspection System have been developed for manufacturing and quality inspection and have been used by industry for various purposes from simple image digitization and processing, luminance adjustment, data and file format conversion, generation of control files for numerical control in laser cuffing, to direct control of robot movement, and interactive as well as automatic machine part measurement.

This article demonstrates the application of time-lapsed imaging and image processing to inform the supercritical processing of tissue scaffolds that are integral to many regenerative therapies. The methodology presented provides online quantitative evaluation of the complex process of scaffold formation in supercritical environments. The capabilities of the developed system are demonstrated through comparison of scaffolds formed from polymers with different molecular weight and with different venting times. Visual monitoring of scaffold fabrication enabled key events in the supercritical processing of the scaffolds to be identified including the onset of polymer plasticization, supercritical points and foam formation. Image processing of images acquired during the foaming process enabled quantitative tracking of the growing scaffold boundary that provided new insight into the nature of scaffold foaming. Further, this quantitative approach assisted in the comparison of different scaffold fabrication protocols. Observed differences in scaffold formation were found to persist, post-fabrication as evidenced by micro x-ray computed tomography (micro x-ray CT) images. It is concluded that time-lapsed imaging in combination with image processing is a convenient and powerful tool to provide insight into the scaffold fabrication process.

Directly performing optical analog computations and image processing in space, such as optical differential operations and image edge detection, is a burgeoning area. To avoid the bulkiness and low efficiency of traditional 4 f filtering systems, one can utilize Green's function and metasurfaces for advanced wavefront control. However, some metasurface differentiators can be hindered by issues like polarization sensitivity, restricted bandwidth, low resolution, and the need for additional polarization devices or digital post‐processing, potentially degrading their performance and operation efficiency. In this work, a dual‐polarization Laplace differentiator is engineered to address these issues based on nonlocal hollow metasurface. The optical transfer function (OTF) required by the Laplace operation can be obtained by exciting different quasi‐bound states in the continuum (Q‐BIC) modes with distinct angular dispersion capabilities under p ‐ and s ‐polarized illumination, respectively. This Laplace differentiator not only directly realizes 2D second‐order edge detection in a dual‐polarization channel but also features a numerical aperture (NA) with an upper limit close to 0.42 and a broadband range reaching 165 nm. Such an efficient, high‐quality dual‐polarization and bandwidth image edge detection approach offers powerful imaging techniques for applications in machine vision, microscopic imaging, and image processing.

Image processing operations may be performed in either the spatial domain or the frequency domain. Spatial domain image processing is attractive because it avoids the mathematical transformations required by frequency domain processing is computationally more efficient and is more intuitive. Historically spatial processing has been done by traversing the image in strict raster-scan fashion (left-to-right top-to-bottom a paradigm encouraged by the syntax of traditional Fortran-like programming languages. Cellular logic is a different way of thinking about spatial processing in which images are modified h defining a set of logical operations to be performed on small groups of pixels neighboring any general pixel in the image. The same operations are then performed on every pixel in the image all at once there is no implied ordering in the processing ofthe pixels. This means that cellular logic has a natural parallelism making it easy to implement on parallel computers. Cellular logic is also efficient even on single-processor machines. since pixels are processed in machine-word sized groups. Another interesting property of cellular logic is that it is Turing-complete: in other words it has the same computational power as any programming language in general so that arbitrarily complex operations may be constructed from a small number of simple logical primitives. In addition the bit-block transfer instruction (bitbit) offers a convenient means of implementing cellular logic functions in software. As its name implies bitbit transfers© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Quantum image processing represents a pivotal field in quantum remote sensing[1-3]. Dr. Siwen Bi and his research team pioneered the concept of quantum image data processing, having dedicated nearly 15 years to this technological advancement. This study focuses on developing core technologies for quantum-enhanced image processors, with particular emphasis on optimizing quantum enhancement algorithms, refining image processing methodologies, and implementing hardware solutions. The research aims to enhance both efficiency and quality in remote sensing and aerospace imaging applications[4-5], while providing efficient and precise image enhancement solutions for medical imaging, industrial inspection, remote sensing processing, and surveying fields. This study integrates quantum computing with traditional image processing technologies to develop quantum image enhancement algorithms, which have been applied to diverse imaging data types including remote sensing images, aerospace imagery, and medical imaging[6-8]. Through systematic research and experiments[9-11], we analyze key technologies in image enhancement processes such as quantum states, quantum superposition, quantum parallel computing, deep learning optimization, and hardware architecture design. Simultaneously, performance evaluations of various algorithms and hardware configurations were conducted using a combination of simulations and experimental methods. The research demonstrates that quantum image enhancement algorithms exhibit significant advantages over traditional methods in terms of processing accuracy, speed, and efficiency. Notably, image quality shows marked improvement during complex image processing. Furthermore, hardware-optimized quantum image processors can effectively support real-time processing of large-scale imaging data. As a cutting-edge image processing method, quantum image enhancement technology has demonstrated remarkable potential in remote sensing, aerospace, and medical fields[12-15]. Quantum computing offers innovative approaches to image processing, where the integration of deep learning and quantum computing can significantly enhance both efficiency and quality. With continuous advancements in quantum imaging hardware, this technology is poised to provide more efficient and precise solutions for image processing applications across various industries.

A Wavelet Transform (WT) is a mathematical technique that transforms a signal into different frequency components, each analyzed with a resolution that matches its scale. Wavelets are small waves with limited duration and they possess both time and frequency localization, which means they can capture both high-frequency and low-frequency information simultaneously. There are several types of wavelet transforms, and, depending on the application, one may be preferred to the others. For a continuous input signal, the time and scale parameters can be continuous leading to the Continuous Wavelet Transform (CWT) and Discrete wavelets. Image processing is a critical area in modern computing with applications ranging from medical imaging to remote sensing and computer vision. Traditional Fourier analysis has limitations in capturing localized features in images due to its global nature. Wavelet Transform (WT), with its multi-resolution and localized time-frequency representation, provides a powerful alternative for analysing and processing visual information. This paper presents a comprehensive overview of the theory of wavelet transforms, their mathematical foundations, and applications in image processing and signal processing, including compression of images, denoising and feature extraction from images.

The purpose of this paper is to analyze the application of image processing in UAV and autopilot. UAV and autonomous driving technology are important development directions in the field of intelligent transportation and agricultural production, and image processing, as one of the core technologies supporting its perception and decision-making, plays a vital role. For UAV, in agricultural plant protection and natural disaster monitoring, the rapid evaluation and identification of farmland growth status and disaster situation can be realized through image processing technology, which provides important support for crop production and rescue work. For the automatic driving system, image processing technology can detect and track the road environment, traffic signs and pedestrians, and improve the driving safety and comfort of vehicles. However, image processing technology also faces some challenges in application, such as complex environmental conditions and real-time requirements. In the future, through the improvement of sensor technology, algorithm optimization and data sharing, the application of image processing technology in UAV and autonomous driving will be continuously improved, bringing more innovation and development opportunities for intelligent transportation and agricultural production.

Image processing plays an important role in computer science, making complex image manipulation more feasible. It can be used either in a general manner or in specific domains such as for medical purposes. With the availability of the internet, image processing applications can be distributed to be available for different people, regardless of their geographical location. Although there are a handful of professional image processing solutions available, there is no well-designed framework that can be simply distributed and customized to be used in specific areas. Other than image processing tools, image storage and retrieval is another issue that needs to be addressed. This research proposes a general platform that can be used for implementing a distributed image processing framework. It provides solutions for storing and retrieving images, particularly large-size ones as well as an approach for executing image processing functions that are physically located anywhere within the distributed system.