Parallel Image Processing: Taking Grayscale Conversion Using OpenMP as an Example

In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularly noteworthy in the field of image processing, which witnessed significant advancements. This parallel computing project explored the field of parallel image processing, with a focus on the grayscale conversion of colorful images. Our approach involved integrating OpenMP into our framework for parallelization to execute a critical image processing task: grayscale conversion. By using OpenMP, we strategically enhanced the overall performance of the conversion process by distributing the workload across multiple threads. The primary objectives of our project revolved around optimizing computation time and improving overall efficiency, particularly in the task of grayscale conversion of colorful images. Utilizing OpenMP for concurrent processing across multiple cores significantly reduced execution times through the effective distribution of tasks among these cores. The speedup values for various image sizes highlighted the efficacy of parallel processing, especially for large images. However, a detailed examination revealed a potential decline in parallelization efficiency with an increasing number of cores. This underscored the importance of a carefully optimized parallelization strategy, considering factors like load balancing and minimizing communication overhead. Despite challenges, the overall scalability and efficiency achieved with parallel image processing underscored OpenMP’s effectiveness in accelerating image manipulation tasks.

New multi-DSP parallel computing architecture for real-time image processing

The flexibility of traditional image processing system is limited because those system are designed for specific applications. In this paper, a new TMS320C64x-based multi-DSP parallel computing architecture is presented. It has many promising characteristics such as powerful computing capability, broad I/O bandwidth, topology flexibility, and expansibility. The parallel system performance is evaluated by practical experiment.

Image processing algorithm acceleration using reconfigurable macro processor model

The concept and advantage of reconfigurable technology is introduced. A kind of processor architecture of re configurable macro processor (RMP) model based on FPGA array and DSP is put forward and has been implemented. Two image algorithms are developed: template-based automatic target recognition and zone labeling. One is estimating for motion direction in the infrared image background, another is line picking-up algorithm based on image zone labeling and phase grouping technique. It is a kind of 'hardware' function that can be called by the DSP in high-level algorithm. It is also a kind of hardware algorithm of the DSP. The results of experiments show the reconfigurable computing technology based on RMP is an ideal accelerating means to deal with the high-speed image processing tasks. High real time performance is obtained in our two applications on RMP.

APPLICATION OF MVP IN REAL TIME IMAGE PROCESSING

MVP is a digital signal processor, which is of MIMD structure and fit for multimedia application. MVP has several processors in it, and its operation is characteristic of parallelism and pipeline; therefore, real-time signal processing can be done on it. This paper presents the image processing system based on MVP, explains the principles of parallel task assignment and hardware pipeline design, and gives out the example of target tracking and edge detection.

Animal Classification System Based on Image Processing &Support Vector Machine

This project is mainly focused to develop system for animal researchers & wild life photographers to overcome so many challenges in their day life today. When they engage in such situation, they need to be patiently waiting for long hours, maybe several days in whatever location and under severe weather conditions until capturing what they are interested in. Also there is a big demand for rare wild life photo graphs. The proposed method makes the task automatically use microcontroller controlled camera, image processing and machine learning techniques. First with the aid of microcontroller and four passive IR sensors system will automatically detect the presence of animal and rotate the camera toward that direction. Then the motion detection algorithm will get the animal into middle of the frame and capture by high end auto focus web cam. Then the captured images send to the PC and are compared with photograph database to check whether the animal is exactly the same as the photographer choice. If that captured animal is the exactly one who need to capture then it will automatically capture more. Though there are several technologies available none of these are capable of recognizing what it captures. There is no detection of animal presence in different angles. Most of available equipment uses a set of PIR sensors and whatever it disturbs the IR field will automatically be captured and stored. Night time images are black and white and have less details and clarity due to infrared flash quality. If the infrared flash is designed for best image quality, range will be sacrificed. The photographer might be interested in a specific animal but there is no facility to recognize automatically whether captured animal is the photographer’s choice or not.

Automatic Detection of Steel Ball's Surface Flaws Based on Image Processing

A new method to detect steel ball's surface flaws is presented based on computer techniques of image processing and pattern recognition. The steel ball's surface flaws is the primary factor causing bearing failure. The high efficient and precision detections for the surface flaws of steel ball can be conducted by the presented method, including spot, abrasion, burn, scratch and crack, etc. The design of main components of the detecting system is described in detail including automatic feeding mechanism, automatic spreading mechanism of steel ball's surface, optical system of microscope, image acquisition system, image processing system. The whole automatic system is controlled by an industrial control computer, which can carry out the recognition of flaws of steel ball's surface effectively.

Automatic Traffic Using Image Processing

The frequent traffic jams at major intersections call for an effective management system. The paper suggests implementing a smart traffic controller using real-time image processing. The sequence of the camera is analyzed using different edge detection algorithms and object counting methods. Previously they used matching method that means the camera will be installed along with traffic light. It will capture the image sequence. To set an image of an empty road as a reference image, the captured images are sequentially matched using image matching;but in my paper, we used filtering method, which filtered the image and released all waste objects and only showed the cars, and after it well showed the number of cars in image. My paper is software that takes a picture or video. It has been customized to be used in the future to control the traffic light sign by giving each sign sufficient time, depending on the number of cars on each direction.

Hadoop and Its Role in Modern Image Processing

This paper introduces MapReduce as a distributed data processing model using open source Hadoop framework for manipulating large volume of data. The huge volume of data in the modern world, particularly multimedia data, creates new requirements for processing and storage. As an open source distributed computational framework, Hadoop allows for processing large amounts of images on an infinite set of computing nodes by providing necessary infrastructures. This paper introduces this framework, current works and its advantages and disadvantages.

Sonar Image Processing System for an Autonomous Underwater Vehicle(AUV)

Sonar image processing system is an important intelligent system of Autonomous Un-derwater Vehicle.Based on TMS320C30 high speed DSP,it is used to realize sonar imagecompression and underwater object detections including obstacle recognition in real time.Inthis paper,the software and hardware designs of this system are introduced and the experi-mental results are given.

Efficient quantum arithmetic operation circuits for quantum image processing

Efficient quantum circuits for arithmetic operations are vital for quantum algorithms.A fault-tolerant circuit is required for a robust quantum computing in the presence of noise.Quantum circuits based on Clifford+T gates are easily rendered faulttolerant.Therefore,reducing the T-depth and T-Count without increasing the qubit number represents vital optimization goals for quantum circuits.In this study,we propose the fault-tolerant implementations for TR and Peres gates with optimized T-depth and T-Count.Next,we design fault-tolerant circuits for quantum arithmetic operations using the TR and Peres gates.Then,we implement cyclic and complete translations of quantum images using quantum arithmetic operations,and the scalar matrix multiplication.Comparative analysis and simulation results reveal that the proposed arithmetic and image operations are efficient.For instance,cyclic translations of a quantum image produce 50%T-depth reduction relative to the previous best-known cyclic translation.

Important Issues Concerning Computation of Variant Templates for Image Processing

This paper presents new insights into our previous research on efficient computation of variant templates. The important aspects are analysed in detail, such as convergence, error control and effectiveness of the Taylors series form of some commonly seen transcendental functions in low level image processing operations. The influence of hardware, as well as the limitations of the proposed approach, is also discussed.

Underwater image clarifying based on human visual colour constancy using double-opponency

Underwater images are often with biased colours and reduced contrast because of the absorption and scattering effects when light propagates in water.Such images with degradation cannot meet the needs of underwater operations.The main problem in classic underwater image restoration or enhancement methods is that they consume long calcu-lation time,and often,the colour or contrast of the result images is still unsatisfied.Instead of using the complicated physical model of underwater imaging degradation,we propose a new method to deal with underwater images by imitating the colour constancy mechanism of human vision using double-opponency.Firstly,the original image is converted to the LMS space.Then the signals are linearly combined,and Gaussian convolutions are per-formed to imitate the function of receptive fields(RFs).Next,two RFs with different sizes work together to constitute the double-opponency response.Finally,the underwater light is estimated to correct the colours in the image.Further contrast stretching on the luminance is optional.Experiments show that the proposed method can obtain clarified underwater images with higher quality than before,and it spends significantly less time cost compared to other previously published typical methods.

Study on the Medical Image Distributed Dynamic Processing Method

To meet the challenge of implementing rapidly advanced, time-consuming medical image processing algorithms, it is necessary to develop a medical image processing technology to process a 2D or 3D medical image dynamically on the web. But in a premier system, only static image processing can be provided with the limitation of web technology. The development of Java and CORBA (common object request broker architecture) overcomes the shortcoming of the web static application and makes the dynamic processing of medical images on the web available. To develop an open solution of distributed computing, we integrate the Java, and web with the CORBA and present a web-based medical image dynamic processing methed, which adopts Java technology as the language to program application and components of the web and utilies the CORBA architecture to cope with heterogeneous property of a complex distributed system. The method also provides a platform-independent, transparent processing architecture to implement the advanced image routines and enable users to access large dataset and resources according to the requirements of medical applications. The experiment in this paper shows that the medical image dynamic processing method implemented on the web by using Java and the CORBA is feasible.

Medical Image Dynamic Collaborative Processing on the Distributed Environment

A new trend in the development of medical image processing systems is to enhance the shar-ing of medical resources and the collaborative processing of medical specialists. This paper presents an architecture of medical image dynamic collaborative processing on the distributed environment by combining the JAVA, CORBA (Common Object Request and Broker Architecture) and the MAS (Multi-Agents System) collaborative mechanism. The architecture allows medical specialists or applications to share records and cornmunicate with each other on the web by overcoming the shortcut of traditional approach using Common Gateway Interface (CGI) and client/server architecture, and can support the remote heterogeneous systems collaboration. The new approach im-proves the collaborative processing of medical data and applications and is able to enhance the in-teroperation among heterogeneous system. Research on the system will help the collaboration and cooperation among medical application systems distributed on the web, thus supply high quality medical service such as diagnosis and therapy to practicing specialists regardless of their actual geo-graphic location.

Using Graphics Processing Units to Parallelize the FDK Algorithm for Tomographic Image Reconstruction

The paper presents the implementation of a parallel version of FDK （Felkamp, David e Kress） algorithm using graphics processing units. Discussion was briefly some elements the computed tomographic scan and FDK algorithm; and some ideas about GPUs （Graphics Processing Units） and its use in general purpose computing were presented. The paper shows a computational implementation of FDK algorithm and the process of parallelization of this implementation. Compare the parallel version of the algorithm with the sequential version, used speedup as a performance metric. To evaluate the performance of parallel version, two GPUs, GeForce 9400GT （16 cores） a low capacity GPU and Quadro 2000 （192 cores） a medium capacity GPU was reached speedup of 3.37.

Application of Computer Image Processing to the Analysis of Fossil Plant Leaves

Fossil plants play an important role in geoscience, such as in the determination of strata and analysis of evolution of natural environment. The vestiges of plant leaves embedded in rocks are usually blurred. This makes manual analysis difficult. The computer image processing and analysis has the following advantages: (i)enhancing useful information of

SAR image despeckling based on edge detection and nonsubsampled second generation bandelets

To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.

Julie-Radiography Quick Image直接数字摄影技术在成人磨牙邻面龋诊断中的应用

目的:探讨Ju lie-Rad iography Qu ick Im age直接数字摄影系统图像处理技术在邻面龋诊断中的临床价值,为邻面龋的临床诊断和治疗提供参考。方法:选择邻面龋的病人拍摄数字化X线片,经过降噪处理,按病人的计算机编号随机排列,每次由3位观察者选用一种图像格局共同阅片,不同图像格局的阅片间隔1周以上,以排除记忆干扰。结果:第一磨牙在4种图像处理格局下邻面龋的诊断符合率高。第二磨牙的正片、负片、伪3D图像处理格局下邻面龋的诊断符合率高,伪彩图像处理格局下邻面龋的诊断符合率低。第三磨牙的正片、伪彩图像处理格局下邻面龋的诊断符合率高,负片、伪3D图像处理格局下邻面龋的诊断符合率低。结论:直接数字化X线片摄影系统的图像处理技术可以用于邻面龋临床辅助诊断。

Fast distributed and parallel pre-processing on massive satellite data using grid computing

Distributed/parallel-processing system like sun grid engine(SGE) that utilizes multiple nodes/cores is proposed for the faster processing of large sized satellite image data. After verification, distributed process environment for pre-processing performance can be improved by up to 560.65% from single processing system. Through this, analysis performance in various fields can be improved, and moreover, near-real time service can be achieved in near future.

A novel algorithm for image perimeter computation based on non-symmetry anti-packing representation

The non-symmetry anti-packing image representation （NAIR） uses a sequence of the instances of some predefined prototypes to represent an image. While significantly reducing the instances required to represent an image in contrary to the quadtree and the linear quadtree, however, NAIR has lost the explicit space relationship among these instances and hence made some geometric operations such as perimeter computation hard to be implemented. In this paper, longitude and latitude grid （L^2G）, a data structure which can restore lost space relationship from the NAIR is first presented, and then a novel algorithm to compute the perimeters of the images represented by the NAIR is presented. The experimental results show that the new algorithm has saved at least 90% of the running time comparing with that based on the quadtree.