A Versatile High-speed Image Processing System Based on DSP and CPLD

In this paper, we present an optimized design method for high-speed embedded image processing system using 32 bit floating-point Digital Signal Processor （DSP） and Complex Programmable Logic Device （CPLD）. The DSP acts as the main processor of the system： executes digital image processing algorithms and operates other devices such as image sensor and CPLD. The CPLD is used to acquire images and achieve complex logic control of the whole system. Some key technologies are introduced to enhance the performance of our system. In particular, the use of DSP/BIOS tool to develop DSP applications makes our program run much more efficiently. As a result, this system can provide an excellent computing platform not only for executing complex image processing algorithms, but also for other digital signal processing or multi-channel data collection by choosing different sensors or Analog-to-Digital （A/D） converters.

A Flexible DSP-Based Network forReal-Time Image-Processing

This paper proposed a general purpose real-time image processing system based on a flexible DSP-based Network, which is implemented by a high bandwidth communication channel, links. The links is realized using FPGA and provides a bandwidth of 12. 8 Gbit/s. Using the links, The topologic of multi-DSP system can be changed online to meet the variabilities of the parallel algorithm of image processing. The system can be assembled with utmost tens of boards and maintain the high communication speed. Analysis of the system adaptivity to image processing is testified followed by actual results. Key words real-time image processing - multi-DSP - flexible - scalable - FPGA - links CLC number TP 303 Foundation item: Supported by the National Natural Science Foundation of China (60135020)Biography: MAO Hai-cen(1973-), male, Ph.D. candidate, research direction: artificial intelligence, expert system, pattern recognition and image processing

基于TMS320C6203 DSP的实时红外图像处理系统

遵从模块化设计思想,提出了以 TI 公司的高性能数字信号处理器 TMS320C6203 为核心器件的实时红外图像处理系统的设计方案,结合大规模可编程逻辑阵列 CPLD 进行逻辑控制和现场可编程门阵列 FPGA 对采集的红外图像进行预处理,完成对图像的实时采集和图像目标的实时处理。

基于C6711 DSP的实时数字稳像系统

为了实现实时便携式脱机稳像,设计了一种基于TI公司C6711DSP芯片的实时数字图像稳定系统.该系统由两个双端口RAM芯片作为数据输入和输出的高速缓存,通过4个Bank信号在8bit和32bit间进行数据转换,由CPLD进行处理逻辑和视频同步控制.系统工作时,通过对片内cache中数据区和程序区的合理分配,并使用EDMA技术进行数据传输,达到高速处理的目的.结果表明,使用内联函数和线性流水技术,可加快算法软件的执行速度,实现了25帧/s的实时图像稳定处理.

一种基于TMS320VC33 DSP的通用图像处理系统设计

介绍了一种以美国TI公司推出的浮点DSP—TMS320VC33为处理器的通用图像处理系统,阐述了该系统的硬件设计思想及软件开发流程。该系统具备良好的算法通用性,是一种优良的图像处理平台。

Automated detection and identification of white-backed planthoppers in paddy fields using image processing

A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers （WBPHs, Sogatella furcifera （Horvath）） and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient （HOG） features and a support vector machine （SVM） classifier that was trained on Gabor and Local Binary Pattern （LBP） features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.

基于C6455 DSP的消像旋实时图像处理系统设计

针对通用计算机消像旋处理实时性差和性价比低的特点,提出了采用C6455 DSP芯片为核心处理器,研究设计了基于C6455 DSP的消像旋实时图像处理系统,实现了图像数据的采集、消像旋处理、输出等一系列功能。结果表明,该系统实时性好,图像显示效果良好,并已成功应用于某光电系统的消像旋处理中。

Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceivedfrom the lack of treatment, ineffective early diagnosis techniques, and mostimportantly its fatality rate. Globally, cancer is the second leading cause ofdeath and among over a hundred types of cancer;lung cancer is the secondmost common type of cancer as well as the leading cause of cancer-relateddeaths. Anyhow, an accurate lung cancer diagnosis in a timely manner canelevate the likelihood of survival by a noticeable margin and medical imagingis a prevalent manner of cancer diagnosis since it is easily accessible to peoplearound the globe. Nonetheless, this is not eminently efficacious consideringhuman inspection of medical images can yield a high false positive rate. Ineffectiveand inefficient diagnosis is a crucial reason for such a high mortalityrate for this malady. However, the conspicuous advancements in deep learningand artificial intelligence have stimulated the development of exceedinglyprecise diagnosis systems. The development and performance of these systemsrely prominently on the data that is used to train these systems. A standardproblem witnessed in publicly available medical image datasets is the severeimbalance of data between different classes. This grave imbalance of data canmake a deep learning model biased towards the dominant class and unableto generalize. This study aims to present an end-to-end convolutional neuralnetwork that can accurately differentiate lung nodules from non-nodules andreduce the false positive rate to a bare minimum. To tackle the problem ofdata imbalance, we oversampled the data by transforming available images inthe minority class. The average false positive rate in the proposed method isa mere 1.5 percent. However, the average false negative rate is 31.76 percent.The proposed neural network has 68.66 percent sensitivity and 98.42 percentspecificity.

基于DSP的信号处理系统设计及应用

为了适应光电图像实时处理速度要求,设计了基于多核数字信号处理(digital signal processor,DSP)和现场可编程门阵列(field programmable gate array,FPGA)的信号处理系统。阐述了该系统的硬件及软件架构设计,图像采用光纤进行传输,可用于处理不同光波段的电荷耦合器件(charge coupled device,CCD)图像数据。以基于可见光CCD传感器的火灾监测系统为例,利用物体燃烧时火焰及烟雾等特征,采用图像特征提取算法对实时采集的图像进行处理,识别出火灾区域后进行上报。实验结果表明,该信号处理系统工作可靠稳定,具有图像高速并行处理能力,能够同时处理多路CCD图像,实现软件在线加载、图像存储卸载等功能,为光电图像处理系统平台提供一种通用解决方案。

High performance reconfigurable hardware system for real-time image processing

A novel reconfigurable hardware system which uses both muhi-DSP and FPGA to attain high performance and real-time image processing are presented. The system structure and working principle of mainly processing multi-BSP board, extended multi-DSP board are analysed. The outstanding advantage is that the communication among different board components of this system is supported by high speed link ports ＆ serial ports for increasing the system performance and computational power. Then the implementation of embedded real-time operating systems （RTOS） by us is discussed in detail. In this system, we adopt two kinds of parallel structures controlled by RTOS for parallel processing of algorithms. The experimental results show that exploitive period of the system is short, and maintenance convenient. Thus it is suitable for real-time image processing and can get satisfactory effect of image recognition.

AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

基于DSP模式的计算机图像处理算法研究

基于DSP架构模式,提出了一种CNN卷积神经网络算法,并将其运用到计算机图像处理中。研究过程中,采用DSP技术进行计算机图像获取、算法处理、算例分析和结果优化,大大提高了计算机图像算法处理质量和效率。经过算法测试验证,结果表明,基于DSP数字信号处理器搭建多DSP并行处理架构模式,采用CNN卷积神经网络算法进行计算机图像处理,能够提高图像处理精度。该算法运行时的性能较高,功能低,CPU占用率不高,且DSP计算机处理系统在多DSP并行处理架构模式下进行算法分析,系统的稳健性和可靠性高,能够适应不同规模级别下的计算机图像处理数据集的处理速度、精度、资源消耗和功率要求,可为计算机图像算法处理提供准确、高效、经济的解决方案,对于计算机图像处理算法设计和优化以及应用具有较好的实用参考价值。

基于DSP+FPGA的实时图像去雾增强系统设计

图像作为人类感知世界最直观信息,为我们日常生活带来极大便利。然而,受雾霾、光照差等环境影响,实际成像存在朦胧不清晰等问题。当前对图像去雾增强处理以事后分析居多,难以满足跟踪等实施性要求较高的应用需求。针对此问题,文章设计一款基于DSP+FPGA的实时去雾增强系统。该系统集合了DSP大型数据处理能力和FPGA高实时性的优点,很好地将改进型"暗通道"原理算法移植其中。有效满足了实时去雾增强功能要求,并已应用于实际工程项目。实验证明,该系统工作稳定有效,能有效解决实际工程中雾霾、光照不足给成像造成的影响。

多总线多DSP实时图像处理操作系统的设计与实现

该文针对多总线多 DSP实时图像识别系统 ,设计并实现了一个并行操作系统 .它包括嵌入到 DSP芯片上的操作系统和运行在 PC机上的协议软件两部分 .协议软件提供一个人机界面 ,接收算法的分解信息 ,并将其按一定的数据结构组织 ,再将所有的子任务及其分解信息连接成一个作业 .DSP上的操作系统支持作业从上位机上加载 ,或通过 EPROM加载 .操作系统支持 VXI总线标准 ,并提供了数据通信、任务分配和并发进程管理等功能 .它根据任务分解信息 ,分配硬件资源 ,构造数据流向 ,建立子任务相互间的同步关系 ,完成与上位机的联络并输出结果 .实验结果表明 ,该文设计的硬件及其操作系统能够适应不同并行结构的需要 ,并得到满意的图像并行处理效果 .

多分辨图像融合算法在DSP系统中的实现

介绍在可见光 -长波红外双波段图像融合高速处理平台上实现多分辨图像融合算法的设计方案 .该硬件平台采用高速数字信号处理器 TMS32 0 C6 2 0 1DSP及多种适于高速数字图像处理的结构 ,在软件设计时 ,针对图像多分辨分解与重构运算过程的特点采取了多项优化措施 ,较好地解决了图像融合算法大运算量和数据存储空间与硬件系统实时处理之间的矛盾 。

采用DSP的电视测量跟踪器的研制

采用DSP+FPGA的线性流水阵列结构的数字图像处理器完成目标数据采集和处理,研制了基于DSP的电视测量跟踪器。针对欲测量地标的背景复杂的情况,在图像处理算法上采用基于遗传算法的图像模板匹配算法,有效降低了计算量,提高了匹配精度,取得了较好的实验效果。该算法还具有每帧图像匹配计算时间基本恒定的优点,便于在实际系统中采用。

一种基于DSP+FPGA技术的实时红外图像直方图均衡器

直方图均衡是一种简单但非常有效的图像增强方法 ,已有的直方图均衡方法包括直方图均匀化(HE)、直方图映射法 (HP)和平台直方图均匀化 (PE)等 ,这些方法的实时实现需要极大的数据存储容量和极高的处理速度。并且要在一个系统中同时实现这几种算法 ,仅靠单纯的硬件实现 (如FPGA)或单纯的软件实现 (DSP)都不能取得良好的效果。而如果将FPGA和DSP相结合 ,则既可发挥FPGA的高速优势 ,又可发挥DSP软件的灵活特点 ,这样就实现了一种功能强大的能对不同红外图像进行处理的实时直方图均衡器。

基于DSP系统的超分辨率图像重建技术研究

由于航空光电设备造价与体积等的限制,需要在不改变航空光电设备硬件结构的前提下,获取尽可能清晰的图像或视频。文章提出了基于DSP图像处理系统的超分辨率重建方法,首先利用Fourier-Mellin变换法和Keren算法的联合优化算法进行运动估计;然后利用基于边缘保持的凸集投影简化方法进行超分辨率重建;最终结合DM642的特征,在不降低精度的前提下,对算法进行优化实现。该方法在不增加系统结构体积和成本的前提下,有效地提高了成像系统的分辨力,进而提高系统的目标识别能力。在以DM642为核心嵌入式图像处理平台中实现超分辨率重建实验,所采用的相机分辨率为720×576,整个重建的时间由传统的几分钟甚至几十分钟下降至20s左右。实验结果表明,用本文方法重建出的图像细节明显比单帧插值的图像清晰,图像的平均梯度和信息熵有了明显提高。

基于整型提升小波变换的图像处理及DSP实现

针对数字信号处理器(DSP)的并行特性,提出了一种二维整型提升小波的并行体系结构。该结构采用乒乓缓存策略,使得数据的传输和小波变换能够同时进行;用基于行的列变换方法使得列变换只需少量行变换结果就能进行列变换;用移位操作代替乘法操作,大大减少了算法的运算量。整个结构采用流水线设计,提高了硬件资源的利用率和降低了算法的中间存储量,实现了图像的实时小波变换。实验证明,该算法与原算法相比,速度提高了15倍,达到了每秒85帧;重构图像的峰值信噪比(PSNR)虽然比离散小波变换要低一些,但仍达到了42 dB以上,因此该算法具有广阔的应用前景。