图像彩色化方法研究进展

目的介绍典型图像彩色化的实现方法及其原理,总结分析图像彩色化方法的特点和应用场景,结合最新研究进展,提出图像彩色化技术以后的研究方向和发展趋势。方法按照图像彩色化技术实现手段分类,对国内外图像彩色化技术研究领域的相关方法进行系统介绍,对比分析基于偏微分方程、图像融合、图像分割的图像彩色化方法及其特点。结论在将来的研究工作中,将图像彩色化技术与其他学科结合,利用计算机的快速处理能力、硬件优势以及丰富的软件资源,并根据应用场景和算法特点,选择合适的图像彩色化方法,可以更好地提高图像处理效率和视觉效果,实现图像彩色化的精准应用。     

基于p-Laplace方程的图像彩色化方法

灰度图像的彩色化是图像处理研究领域中富有挑战性的研究课题,且具有十分广阔的应用前景。在分析现有图像彩色化方法的基础上,提出了一种基于p-Laplace方程的图像彩色化新方法。新方法首先由用户在灰度图像上给定少量的颜色条带;然后通过求解p-Laplace方程实现颜色扩散的彩色化。由于p-Laplace算子是一个各向异性扩散的非线性算子,所以与采用偏微分方程方法的泊松方法和拉普拉斯方法相比,图像彩色化在扩展颜色的同时更能保持边缘效果。     

图像处理在自动焊接中的应用和展望

图像处理技术在自动焊接领域的应用已引起国内外学者的广泛重视。通过图像传感技术获取焊接熔池直观丰富的图像信息，使用高效的图像处理算法，提取焊接熔池的特征信息，用以实现自动焊接过程质量实时传感与控制。本文综合论述了图像处理技术在自动焊接中的应用原理、数字图像的采集方法、数字图像的特征信息定义、广义数字图像处理技术及其对自动化焊接理论研究和实践应用的推动作用。通过总结图像处理技术的研究和应用现状，综合分析了图像处理技术在现代焊接技术中发展和应用的前景。     

基于核独立分量分析的PET图像处理

随着医学成像和计算机辅助技术的发展,从二维医学图像到三维可视化技术,医学图像处理技术已经是目前医学技术中发展最快的领域之一。PET/CT系统因为同时具有多层螺旋CT和PET的临床特点,可以同时提供结构和功能方面的信息,在临床上有着广泛的应用前景。对它的图像处理也成了研究热点。因此本文对处理PET图像进行研究。本文研究了基于核独立分量分析的去噪应用于PET图像处理,实验结果表明,相对于医学图像处理中传统ICA去噪算法,本文所采用的去噪算法更适合于PET图像的处理。因此,可以判定Kernel-ICA在PET图像的去噪处理中是一种非常实用而且有效的算法,进而也可以推断出在PET图像处理中,基于Kernel-ICA的去噪算法具有很好的发展前景。     

高分辨电子显微像图像处理技术的应用

对高分辨电子显微像图像处理技术进行了比较详细的分类.简要分析了在测定晶体结构的过程中,高分辨电子显微像与图像处理技术相结合的必要性.论述了尝试法、出射波重构法和解卷技术3种主要的图像处理方法,介绍了该类图像处理技术应用于材料学研究中的典型事例,并讨论了其应用范围、前景及其局限性.     

图像Inpainting技术原理及在包装印刷图像处理中的应用

在包装印刷行业图像修复问题需要有经验的技术人员进行复杂的手工处理,随着计算机图像处理领域对图像自动处理技术的讨论,INPAINTING技术对包装印刷图像处理提供了新的方法和方向.主要介绍了图像自动修复技术的原理、发展,以及在包装印刷行业的应用.     

图像处理技术在纺织业的应用

随着计算机技术的快速发展,计算机技术在各个领域、行业中得到了广泛的推广应用,本文主要针对计算机图像处理技术在纺织业的应用情况进行了全面的分析,并结合在纺织工业中常用的图像处理方法对计算机图像处理技术进行深入的分析与评价,这也为日后实现纺织工业自动化检测的进行探索,通过对其分析并且确定了在纺织业中应用计算机图像处理技术的实际意义,最后对计算机图像处理技术在纺织业的发展前景进行了预测。     

图像处理与识别在果蔬成熟度监测中的研究及应用

目的 分析了果蔬成熟度自动监测对发展智慧农业的重要意义,对图像处理与识别技术在监测果蔬成熟度领域的研究与应用现状进行综述、总结与展望,以期为我国发展果蔬成熟度在线或自动检测识别技术提供参考。方法 对图像处理与识别在监测果蔬成熟度中的原理、优势进行分析,对特征提取、深度学习中的神经网络在该领域中的应用研究进展进行综述。结果 采用以图像处理和识别为核心的计算机视觉检测技术对果蔬的颜色、纹理等外部特征进行成熟度检测具有优势,结合神经网络对果蔬成熟度进行检测的识别率高,可在采摘、运输等场景对果蔬成熟度进行监测。结论 图像处理与识别技术在果蔬成熟度监测领域有望得到突破,将催生更多新的应用场景。     

“图像处理及相关的数学问题”国际研讨班

随着计算机技术和网络技术的飞速发展，图像技术在因特网上的应用发展迅猛，在图像处理领域出现了许多有待研究的问题，其中相当一部分在本质上是数学问题。可以说，与图像处理相关的数学问题的逐步解决，将为图像技术的发展开拓新的空间。为了进一步理解图像处理领域中涉及的数学问题，并使数学研究人员对相关的图像处理问题的工程背景有所了解，我们定于2004年5月16日至2004年5月28日在浙江大学数学科学研究中心举办“图像处理及相关的数学问题”国际研讨班，为期两周。     

计算机视觉中的尺度空间方法

近年来,偏微分方程、变分法和数学形态学等现代数学方法被广泛应用于计算机视觉领域,尺度空间方法作为这些方法的统一框架,已逐渐成为国际上计算机视觉和图像处理领域研究的热点.本文综述尺度空间方法的基本思想、理论基础、视觉处理能力及实现方法,然后提出尺度空间方法理论和应用值得研究的若干问题.     

High-speed MRF-based segmentation algorithm using pixonal images

Abstract

Segmentation is one of the most complicated procedures in the image processing that has important role in the image analysis. In this paper, an improved pixon-based method for image segmentation is proposed. In proposed algorithm, complex partial differential equations (PDEs) is used as a kernel function to make pixonal image. Using this kernel function causes noise on images to reduce and an image not to be over-segment when the pixon-based method is used. Utilising the PDE-based method leads to elimination of some unnecessary details and results in a fewer pixon number, faster performance and more robustness against unwanted environmental noises. As the next step, the appropriate pixons are extracted and eventually, we segment the image with the use of a Markov random field. The experimental results indicate that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-image segmentation techniques. To evaluate the proposed algorithm and compare it with the last best algorithms, many experiments on standard images were performed. The results indicate that the proposed algorithm is faster than other methods, with the most segmentation accuracy.     

Jump regression,image processing,and quality control

ABSTRACT

Images have been widely used in manufacturing applications for monitoring production processes, partly because they are often convenient and economic to acquire by different types of imaging devices. Medical imaging techniques, such as CT, PET, X-ray, ultrasound, magnetic resonance imaging (MRI), and functional MRI, have become a basic medical diagnosis tool nowadays. Satellite images are also commonly used for monitoring the changes of the earth’s surface. In all these applications, image comparison and monitoring are the common and fundamentally important statistical problems that should be addressed properly. In computer science, applied mathematics, statistics and some other disciplines, there have been many image processing methods proposed. In this article, I will discuss (i) a powerful statistical tool, called jump regression analysis (JRA), for modeling and analyzing images and other types of data with jumps and other singularities involved, (ii) some image processing problems and methods that are potentially useful for image comparison and monitoring, and (iii) some of my personal perspectives about image comparison and monitoring.     

Online variational learning of finite inverted Beta-Liouville mixture model for biomedical analysis

Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X-ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta-Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.     

Automated segmentation of synchrotron radiation micro-computed tomography biomedical images using Graph Cuts and neural networks

Synchrotron Radiation (SR) X-ray micro-Computed Tomography (μCT) enables magnified images to be used as a non-invasive and non-destructive technique with a high space resolution for the qualitative and quantitative analyses of biomedical samples. The research on applications of segmentation algorithms to SR-μCT is an open problem, due to the interesting and well-known characteristics of SR images for visualization, such as the high resolution and the phase contrast effect. In this article, we describe and assess the application of the Energy Minimization via Graph Cuts (EMvGC) algorithm for the segmentation of SR-μCT biomedical images acquired at the Synchrotron Radiation for MEdical Physics (SYRMEP) beam line at the Elettra Laboratory (Trieste, Italy). We also propose a method using EMvGC with Artificial Neural Networks (EMANNs) for correcting misclassifications due to intensity variation of phase contrast, which are important effects and sometimes indispensable in certain biomedical applications, although they impair the segmentation provided by conventional techniques. Results demonstrate considerable success in the segmentation of SR-μCT biomedical images, with average Dice Similarity Coefficient 99.88% for bony tissue in Wistar Rats rib samples (EMvGC), as well as 98.95% and 98.02% for scans of Rhodnius prolixus insect samples (Chagas's disease vector) with EMANNs, in relation to manual segmentation. The techniques EMvGC and EMANNs cope with the task of performing segmentation in images with the intensity variation due to phase contrast effects, presenting a superior performance in comparison to conventional segmentation techniques based on thresholding and linear/nonlinear image filtering, which is also discussed in the present article.     

A study of the feature‐based digital image stabilization system

Abstract

This article presents a digital image stabilization scheme that uses image processing techniques to compensate for undesirable image jitter due to vehicle or platform vibration to obtain a stabilized image display. The developed digital image stabilization system is based on the image combination approach combined with advanced image selection, feature detection, feature matching and pre‐rotation processing to produce crisp images. Using the Sobel edge detector, we calculate the magnitude of edge response as the judgment criteria for image transformation. To demonstrate the validity of the proposed technique, experimental results are given for some synthetic images.     

Topographical heterogeneity in transparent PVA hydrogels studied by AFM

Physically crosslinked poly (vinyl alcohol) (PVA) hydrogels have a wide range of biomedical applications. Transparent and stable PVA hydrogels synthesized by freeze-thawing method are potential candidates to be used as tissue engineering scaffolds provided they exhibit suitable topographical roughness and surface energy. The effect of processing parameters i.e., polymer concentration and number of freeze-thaw cycles on the resulting topography of the freeze-thawed transparent hydrogels has been studied and quantified using non-contact mode of an atomic force microscope (AFM) and image analysis. Simultaneously captured phase contrast images have revealed significant information about morphological changes in the topographical features and crystallinity of the hydrogels. Topographical roughness was found to decrease as a function of number of freeze-thaw cycles.     

A fast BNM (Best Neighborhood Matching): Algorithm and parallel processing for image restoration

Best Neighborhood Matching (BNM) algorithm is a good approach of error concealment in terms of restored image quality. However, this kind of error concealment algorithm is commonly computation‐intensive, which restricts their real applications on large‐scale image or video sequence restoration. In this article, we propose a fast method, named Jump and look around Best Neighborhood Matching (JBNM), which reduces computing time to one sixth of that by BNM, while the quality of the restored images remains almost the same. To further reduce processing time and meet large‐scale image restorations, a parallel JBNM working on a cluster of workstations is proposed. Several critical techniques, including reading policy, overlap stripe data distribution, and communication strategies, have been developed to obtain high performance. Both theoretical analysis and experiment results indicate that our parallel JBNM provides an efficient technique for image restoration applications. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 13, 189–200, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10057     

生物医学图像信息技术相关研究

生物医学图像信息技术是一门计算机技术与生物医学相结合的综合性学科,其隶属于医学信息学。生物医学图像信息技术的研究对象为生物体,其主要根据不同光源在不同设备中所显示的图像差异,结合现代信息处理技术,实现对图像信息的收集整理、分析、储存、检索、利用和传播。本文主要对现代生物医学图像信息技术的应用领域及发展情况进行了综述。     

Multivariate analysis-based image enhancement model for machine vision inspection

Image enhancement is an essential procedure in machine vision-based inspection. In practical applications, image enhancement is usually a part of image pre-processing, intended to make the following inspection more effective. The image enhancement method is usually selected by trial-and-error or on the basis of experience. This paper presents an automatic procedure for fast and effective image enhancement. The procedure uses multivariate analysis to automatically construct an optimal image enhancement model. First, an optimally enhanced image was selected from the literature as a basis for the model. Then, the image features were identified and Wilks’ statistic was used for feature selection. Next, discriminate functions were built to select the optimal image enhancement method. To verify the model, 53 training images from the literature and 12 test images from a local company were used in an experimental analysis. The model achieved 98.11% accuracy in selecting the most suitable image enhancement method, and the average increase in contrast was 98% for the 53 training images. The enhancement method selection results for the 12 test images were also in agreement with the 53 training images from the literature. The results show that the proposed method is effective and appropriate for quickly improving image contrast.