Formulating efficient software solution for digital image processing system

Digital image processing systems are complex, being usually composed of different computer vision libraries. Algorithm implementations cannot be directly used in conjunction with algorithms developed using other computer vision libraries. This paper formulates a software solution by proposing a processor with the capability of handling different types of image processing algorithms, which allow the end users to install new image processing algorithms from any library. This approach has other functionalities like capability to process one or more images, manage multiple processing jobs simultaneously and maintain the manner in which an image was processed for later use. It is a computational efficient and promising technique to handle variety of image processing algorithms. To promote the reusability and adaptation of the package for new types of analysis, a feature of sustainability is established. The framework is integrated and tested on a medical imaging application, and the software is made freely available for the reader. Future work involves introducing the capability to connect to another instance of processing service with better performance. Copyright © 2015 John Wiley & Sons, Ltd.     

All-in-focus imaging using average filter-based relative focus measure

Digital images are normally taken by focusing on an object, resulting in defocused background regions. A popular approach to produce an all-in-focus image without defocused regions is to capture several input images at varying focus settings, and then fuse them into an image using offline image processing software. This paper describes an all-in-focus imaging method that can operate on digital cameras. The proposed method consists of an automatic focus-bracketing algorithm that determines at which focuses to capture images and an image-fusion algorithm that computes a high-quality all-in-focus image. While most previous methods use the focus measure calculated independently for each input image, the proposed method calculates the relative focus measure between a pair of input images. We note that a well-focused region in an image shows better contrast, sharpness, and details than the corresponding region that is defocused in another image. Based on the observation that the average filtered version of a well-focused region in an image shows a higher correlation to the corresponding defocused region in another image than the original well-focused version, a new focus measure is proposed. Experimental results of various sample image sequences show the superiority of the proposed measure in terms of both objective and subjective evaluation and the proposed method allows the user to capture all-in-focus images directly on their digital camera without using offline image processing software.     

基于边缘的实时图像缩放算法研究

传统的基于图像边缘的插值算法由于自身的复杂性而很少用于实时图像处理,而不基于边缘的插值算法由于低通滤波的效应通常会使插值后的图像出现边缘模糊,影响视觉效果。针对上述问题,提出了一种基于边缘的自适应图像插值算法。该算法以相邻像素的二阶差分为基础,自动选择与目标像素点相对应的源像素组进行插值运算。在Matlab环境中实现了各种图像插值算法,然后对插值结果进行了主客观评价,并对各种算法获得的图像进行了边缘检测及复杂度分析。同时,设计了本文算法的硬件实现结构,并用Verilog语言进行描述,综合出目标代码,最后通过FPGA验证。研究表明,运用该算法插值获得了边缘清晰的目标图像,且该算法复杂度低,便于硬件实现。因此,该算法能有效地实现图像插值,适应于实时条件下的图像缩放。     

Collaborative Personalization of Image Enhancement

This paper presents methods for personalization of image enhancement, which could be deployed in photo editing software and also in cloud-based image sharing services. We observe that users do have different preferences for enhancing images and that there are groups of people that share similarities in preferences. Our goal is to predict enhancements for novel images belonging to a particular user based on her specific taste, to facilitate the retouching process on large image collections. To that end, we describe an enhancement framework that can learn user preferences in an individual or collaborative way. The proposed system is based on a novel interactive application that allows to collect user’s enhancement preferences. We propose algorithms to predict personalized enhancements by learning a preference model from the provided information. Furthermore, the algorithm improves prediction performance as more enhancement examples are progressively added. We conducted experiments via Amazon Mechanical Turk to collect preferences from a large group of people. Results show that the proposed framework can suggest image enhancements more targeted to individual users than commercial tools with global auto-enhancement functionalities.     

开放红外光谱分析系统的架构设计

目前国内的红外光谱软件缺乏统一开放的软件架构模型,使得红外光谱软件的开发缺乏合理的设计和规划,由此产生了诸如软件稳定性、数据格式通用性、软件扩展性等一系列问题.针对这一现状,提出了一种具备灵活性、复用性的红外光谱系统架构设计方案.在软件数据结构方面,借鉴JCAMP-DX国际光谱数据格式标准对光谱数据进行抽象,重新设计了...     

Window memoization: an efficient hardware architecture for high-performance image processing

This work presents a new performance improvement technique, window memoization, for hardware implementations of local image processing algorithms. Window memoization combines the memoization techniques proposed in software and hardware with data redundancy in image processing to improve the efficiency of local image processing algorithms implemented in hardware. It minimizes the number of redundant computations performed on an image by identifying similar neighborhoods of pixels in the image and skipping the redundant computations. We have developed an optimized architecture in hardware that embodies the window memoization technique. Our hardware design for window memoization achieves high speedups with an overhead in hardware area that is significantly less than that of the conventional performance improvement techniques. As case studies in hardware, we have applied window memoization to the Kirsch edge detector and median filter. The typical speedup factor in hardware is 1.58 with 40% less hardware in comparison to conventional optimization techniques.     

A real-time near infrared image acquisition system based on image quality assessment

This paper presents a real-time image acquisition system with an improved image quality assessment module to acquire high-quality near infrared (NIR) images. Thermal imaging plays a vital role in a wide range of medical and military applications. The demand for high-throughput image acquisition and image processing has continuously increased especially for critical medical and military purposes where executions under real-time constraints are required. This work implements an NIR image quality assessment module, which utilizes improved two-dimensional entropy and mask-based edge detection algorithms. The effectiveness of the proposed image quality assessment algorithms is demonstrated through the implementation of a complete finger-vein biometric system. The proposed model is implemented as an embedded system on a field programmable gate array prototyping platform. By including the image quality assessment module, the proposed system is able to achieve a recognition accuracy of 0.87 % equal error rate, and can handle real-time processing at 15 frames/s (live video rate). This is achieved through hardware acceleration of the proposed image quality assessment algorithms via a novel streaming architecture.     

一个基于消息传递接口和面向对象的图像信号处理并行向量库

传统并行软件系统的设计和实现存在着开发效率低、质量难以保证和可移植性差等问题。针对这些问题,采用开发标准并行库的方法加以解决。借鉴高性能嵌入式计算软件计划(high performance embedded computing software initiative,HPEC_SI)的解决方法,基于消息传递接口(message passing interface,MPI)的消息传递机制,对图像/信号处理中的一些典型并行算法以类组件的方式进行封装,设计和实现了具有面向对象特征的、用于图像/信号处理的并行向量库,提供给应用软件开发人员一个良好的开发环境。通过测试和实验证明,该库可以高效地实现相应的向量矩阵并行算法,并具有简单易用、可复用性和可移植性强、效率高的特点。     

A parallel solution for high resolution histological image analysis

This paper describes a general methodology for developing parallel image processing algorithms based on message passing for high resolution images (on the order of several Gigabytes). These algorithms have been applied to histological images and must be executed on massively parallel processing architectures. Advances in new technologies for complete slide digitalization in pathology have been combined with developments in biomedical informatics. However, the efficient use of these digital slide systems is still a challenge. The image processing that these slides are subject to is still limited both in terms of data processed and processing methods. The work presented here focuses on the need to design and develop parallel image processing tools capable of obtaining and analyzing the entire gamut of information included in digital slides. Tools have been developed to assist pathologists in image analysis and diagnosis, and they cover low and high-level image processing methods applied to histological images. Code portability, reusability and scalability have been tested by using the following parallel computing architectures: distributed memory with massive parallel processors and two networks, INFINIBAND and Myrinet, composed of 17 and 1024 nodes respectively. The parallel framework proposed is flexible, high performance solution and it shows that the efficient processing of digital microscopic images is possible and may offer important benefits to pathology laboratories.     

基于分层六角网格对简单三维物体的快速识别

提出了一种利用目前的CRS系统摄取六角网格数字图像的方法,并对获取的图像提出了基于金字塔处理的、从边缘检测等低层处理到模式识别等高层处理的算法.借助串行计算机模拟,利用六角几何和并行处理构架来识别六角网格数字图像中的物体.实验结果表明,该算法能够快速地识别一些简单的三维物体.最后提出了一个2层的六角网格视网膜的设计方案.文中论证了用于图像分析的六角像素网格的鲁棒性.     

A novel image segmentation algorithm based on neutrosophic similarity clustering

Segmentation is an important research area in image processing, which has been used to extract objects in images. A variety of algorithms have been proposed in this area. However, these methods perform well on the images without noise, and their results on the noisy images are not good. Neutrosophic set (NS) is a general formal framework to study the neutralities’ origin, nature, and scope. It has an inherent ability to handle the indeterminant information. Noise is one kind of indeterminant information on images. Therefore, NS has been successfully applied into image processing algorithms. This paper proposed a novel algorithm based on neutrosophic similarity clustering (NSC) to segment gray level images. We utilize the neutrosophic set in image processing field and define a new similarity function for clustering. At first, an image is represented in the neutrosophic set domain via three membership sets: T, I and F. Then, a neutrosophic similarity function (NSF) is defined and employed in the objective function of the clustering analysis. Finally, the new defined clustering algorithm classifies the pixels on the image into different groups. Experiments have been conducted on a variety of artificial and real images. Several measurements are used to evaluate the proposed method's performance. The experimental results demonstrate that the NSC method segment the images effectively and accurately. It can process both images without noise and noisy images having different levels of noises well. It will be helpful to applications in image processing and computer vision.     

Weight assignment for adaptive image restoration by neural networks

This paper presents a scheme for adaptively training the weights, in terms of varying the regularization parameter, in a neural network for the restoration of digital images. The flexibility of neural-network-based image restoration algorithms easily allow the variation of restoration parameters such as blur statistics and regularization value spatially and temporally within the image. This paper focuses on spatial variation of the regularization parameter. We first show that the previously proposed neural-network method based on gradient descent can only find suboptimal solutions, and then introduce a regional processing approach based on local statistics. A method is presented to vary the regularization parameter spatially. This method is applied to a number of images degraded by various levels of noise, and the results are examined. The method is also applied to an image degraded by spatially variant blur. In all cases, the proposed method provides visually satisfactory results in an efficient way.     

Robust feature extraction algorithm suitable for real-time embedded applications

Smart cameras integrate processing close to the image sensor, so they can deliver high-level information to a host computer or high-level decision process. One of the most common processing is the visual features extraction since many vision-based use-cases are based on such algorithm. Unfortunately, in most of cases, features detection algorithms are not robust or do not reach real-time processing. Based on these limitations, a feature detection algorithm that is robust enough to deliver robust features under any type of indoor/outdoor scenarios is proposed. This was achieved by applying a non-textured corner filter combined to a subpixel refinement. Furthermore, an FPGA architecture is proposed. This architecture allows compact system design, real-time processing for Full HD images (it can process up to 44 frames/91.238.400 pixels per second for Full HD images), and high efficiency for smart camera implementations (similar hardware resources than previous formulations without subpixel refinement and without non-textured corner filter). For accuracy/robustness, experimental results for several real-world scenes are encouraging and show the feasibility of our algorithmic approach.     

On fast microscopic browsing of MPEG-compressed video

MPEG has been established as a compression standard for efficient storage and transmission of digital video. However, users are limited to VCR-like (and tedious) functionalities when viewing MPEG video. The usefulness of MPEG video is presently limited by the lack of tools available for fast browsing, manipulation and processing of MPEG video. In this paper, we first address the problem of rapid access to individual shots and frames in MPEG video. We build upon the compressed-video-processing framework proposed in [1, 8], and propose new and fast algorithms based on an adaptive mixture of approximation techniques for extracting spatially reduced image sequence of uniform quality from MPEG video across different frame types and also under different motion activities in the scenes. The algorithms execute faster than real time on a Pentium personal computer. We demonstrate how the reduced images facilitate fast and convenient shot- and frame-level video browsing and access, shot-level editing and annotation, without the need for frequent decompression of MPEG video. We further propose methods for reducing the auxiliary data size associated with the reduced images through exploitation of spatial and temporal redundancy. We also address how the reduced images lead to computationally efficient algorithms for video analysis based on intra- and inter-shot processing for video database and browsing applications. The algorithms, tools for browsing and techniques for video processing presented in this paper have been used by many in IBM Research on more than 30 h of MPEG-1 video for video browsing and analysis.     

Combined new nonnegative matrix factorization algorithms with two-dimensional nonnegative matrix factorization for image processing

In recent years, nonnegative matrix factorization (NMF) has attracted significant amount of attentions in image processing, text mining, speech processing and related fields. Although NMF has been applied in several application successfully, its simple application on image processing has a few caveats. For example, NMF costs considerable computational resources when performing on large databases. In this paper, we propose two enhanced NMF algorithms for image processing to save the computational costs. One is modified rank-one residue iteration (MRRI) algorithm , the other is element-wisely residue iteration (ERI) algorithm. Here we combine CAPG (a NMF algorithm proposed by Lin), MRRI and ERI with two-dimensional nonnegative matrix factorization (2DNMF) for image processing. The main difference between NMF and 2DNMF is that the former first aligns images into one-dimensional (1D) vectors and then represents them with a set of 1D bases, while the latter regards images as 2D matrices and represents them with a set of 2D bases. The three combined algorithms are named CAPG-2DNMF, MRRI-2DNMF and ERI-2DNMF. The computational complexity and convergence analyses of proposed algorithms are also presented in this paper. Three public databases are used to test the three NMF algorithms and the three combinations, the results of which show the enhancement performance of our proposed algorithms (MRRI and ERI algorithms) over the CAPG algorithm. MRRI and ERI have similar performance. The three combined algorithms have better image reconstruction quality and less running time than their corresponding 1DNMF algorithms under the same compression ratio. We also do some experiments on a real-captured image database and get similar conclusions.     

基于贝叶斯网络的遥感数据处理软件

航空航天对地观测系统利用遥感数据获取地表信息的过程中,存在数据误差造成的不确定性和成像时间限制造成的不完整性。针对上述问题该文开发了一款以贝叶斯网络及其相关算法为基础的遥感数据处理软件——BayesNetEX,介绍BayesNetEX的体系结构及使用方法,以三时相北京市TM遥感影像为对象进行变化检测数据处理试验,说明该软件能有效地应用于遥感影像处理。     

A coarse-grain reconfigurable architecture for multimedia applications featuring subword computation capabilities

This paper presents the design and the implementation of a coarse-grain reconfigurable machine used as an accelerator for a programmable RISC core, to speed up the execution of computationally demanding tasks like multimedia applications. We created a VHDL model of the proposed architecture and implemented it on a FPGA board for prototyping purposes; then we mapped on our architecture some DSP and image processing algorithms as a benchmark. In particular, we provided the proposed architecture with subword computation capabilities, which turns out to be extremely effective especially when dealing with image processing algorithms, achieving significant benefits in terms of speed and efficiency in resource usage. To create the configuration bitstream (configware) we created a tool based on a graphical user interface (GUI) which provides a first step towards the automation of the programming flow of our design: the tool is meant to ease the life of the programmer, relieving him from the burden of calculating the configuration bits by hand. Synthesis results indicate that the area occupation and the operating frequency of our design are reasonable also when compared to other similar design. In addition to this, the amount of clock cycles taken by our machine to perform a given algorithm is orders of magnitude smaller than the one required by a corresponding software implementation on a RISC microprocessor.     

医学图像处理与分析软件平台综述

医学图像处理与分析技术可以给医生提供更清晰、更准确的信息,以利于疾病的诊断和鉴别诊断。医学图像分割、医学图像配准以及三维可视化等算法是医学图像处理与分析发展的源动力,而基于这些算法研发的软件平台是医学图像处理与分析前进的加速器。现有的医学图像处理与分析软件平台极大地便利了医学影像领域的研究者与应用者,大大加快了医学影像发展的步伐。对国内外医学图像处理与分析的软件平台进行了比较系统的归纳,详细地介绍了各类主流软件平台,并充分地讨论了各种平台的优缺点,旨在推广医学图像处理与分析软件平台在国内的应用,并激发国内医学影像领域的研究者为医学图像处理与分析软件平台贡献自己的力量。     

A Dual Source,Parallel Architecture for Computer Vision

We present a parallel architecture for object recognition and location based on concurrent processing of depth and intensity image data. Parallel algorithms for curvature computation and segmentation of depth data into planar or curved surface patches, and edge detection and segmentation of intensity data into extended linear features, are described. Using this feature data in comparison with a CAD model, objects can be located in either depth or intensity images by a parallel pose clustering algorithm.The architecture is based on cooperating stages for low/intermediate level processing and for high level matching. Here, we discuss the use of individual components for depth and intensity data, and their realisation and integration within each parallel stage. We then present an analysis of the performance of each component, and of the system as a whole, demonstrating good parallel execution from raw image data to final pose.     

Methods for fast morphological image transforms using bitmapped binary images

In this paper we present new implementations for morphological binary image processing on a general-purpose computer, using a bitmap representation of binary images instead of representing binary images as bitplanes inserted in gray value images. The bitmap data representation is a very efficient one, both in terms of memory requirements and in terms of algorithmic efficiency because of the CPU operates on 32 pixels in parallel. The algorithms described in this paper are capable of performing the basic morphological image transforms using structuring elements of arbitrary size and shape. In order to speed up morphological operations with respect to commonly used, large, convex structuring elements, the logarithmic decomposition of structuring elements is used. Experiments indicate that the new algorithms are more than 30 times faster for pixelwise operations and about an order of magnitude faster for the basic morphological transforms than the fastest known software implementations.