基于三维加权和双域滤波的螺旋CT伪影校正

螺旋CT重建会受到锥束伪影和风车伪影的影响,锥束伪影是由于锥角和螺距过大而导致的,而风车伪影由纵向方向采样不足引起,为了降低锥束伪影与风车伪影对CT图像的影响,提出一种螺旋CT伪影校正算法;首先采用三维加权螺旋FDK算法进行重建,有效去除重建图像中的锥束伪影,然后采用改进的双域滤波算法对含风车伪影图像进行校正;三维加权螺旋FDK算法通过对大锥角的射线给予不利权重来抑制锥束伪影,改进的双域滤波算法可以在去除风车伪影的同时保留更多的细节;计算机仿真实验结果表明,该算法能有效地抑制重建图像中的锥束伪影和风车伪影,提高CT图像的质量。     

X射线工业CT成像过程复杂伪影抑制方法综述

X射线工业计算机断层(Computerized tomography, CT)技术是一种先进的非接触式无损三维检测技术,能在无损伤情况下以灰度图像的形式对物体内部结构进行全面、详细地分析,在航空航天、工业生产、安检等领域发挥着重要的作用.针对工业CT伪影严重降低图像质量问题,对工业CT成像过程复杂伪影形成机理进行分析,对不同类型伪影抑制方法进行归纳总结.阐述了基于射线衰减、探测器及高密度差异、采样数据及重建等不同过程伪影成因及伪影消除相关算法的最新技术进展,并对近年来人工智能深度学习背景下新兴的基于深度学习及神经网络的工业CT无损检测研究与发展方向进行了总结和展望.     

Deep learning-based compressed image artifacts reduction based on multi-scale image fusion

One of the visually noticeable compression artifacts in block-based image/video compression platforms is called blocking artifact. Several post-processing methods were presented to reduce such kind of artifacts. However, most methods in the literature often induce visibly blurring artifacts. The paper presents a deep network to eliminate image compression artifacts (usually denoted by image deblocking) based on image fusion in multi-scale manner. Recent deep learning-based related methods usually learn deep models using a loss function in per-pixel manner based on explicit image priors in order to directly produce clean images. In place of existing deep learning-guided approaches, the problem is reformulated in this paper to the learning of the residuals (or artifacts) between the received images and their corresponding clean images (ground truths). In the presented deep framework, an input image is first down-sampled while naturally reducing the blocking artifacts. Then, our multi-scale image fusion model is used for fusing the different down-scaled versions (of less artifacts) with the input image (with severer artifacts) to estimate the blocking artifacts. Then, by deducting the estimated artifacts from the input image, the blocking artifacts can be significantly eliminated and most original image details are preserved simultaneously. The presented method is well applicable to any vision-based computer systems with digital visual codec embedded.     

Toward a theory of verification and validation: Artifacts

This article uses the notion of artifacts to generate a theory for the verification and validation of intelligent systems. This theory has a number of applications. First, it provides a language and a way of thinking about verification and validation. Second, it suggests that we examine not the processes used in the design and development of systems, but instead the tangible artifacts generated by those processes. Third, it broadens the base of the set of artifacts encompassed by the typical verification and validation processes to include more than just specifications and prototypes. Fourth, the use of artifacts leads to a redefinition of verification and validation in terms of artifacts. Fifth, verification and validation tests are tied to artifacts, so that the choice of a set of artifacts can be used as a means of generating a portfolio of tests to investigate each artifact and the interaction of artifacts. Sixth, relationships between artifacts can be defined as “operators” which can be isolated and investigated for their individual quality. Seventh, standards can be generated for specific artifacts and operators rather than for entire development processes. © 1994 John Wiley & Sons, Inc.     

一种视频编码中的自适应块效应后处理算法

基于块DCT图像压缩方法通常会因比特率低而导致块效应.提出了一种在DCT域自适应消除块效应的算法,首先将块效应模型化为2维阶梯函数,并利用人眼视觉特性检测视频序列块效应的程度,利用索伯算子检测出图像的边缘,根据块效应的可见度对图像进行自适应处理.实验结果表明,本算法性能优于其他算法.     

一种空间域自适应去块效应算法

基于块离散余弦变换的图像压缩编码,在低比特率时其重构图像的块边界上会产生严重的方块效应。提出了一种空间域的自适应去块效应算法。首先把块效应建模成阶梯函数,然后根据判断准则把图像块分成平滑块,纹理块和边缘块。对平滑块,首次导出了块效应强度的表达式,然后根据块效应强度和平滑度进行自适应平滑滤波;对边缘块和纹理块,采用简单的Sigma滤波器对块边界周围的像素滤波。大量的仿真实验结果表明提出的算法有优越的去块效应性能。     

Challenges of working with artifacts in requirements engineering and software engineering

When developing or evolving software systems of non-trivial size, having the requirements properly documented is a crucial success factor. The time and effort required for creating and maintaining non-code artifacts are significantly influenced by the tools with which practitioners view, navigate and edit these artifacts. This is not only true for requirements, but for any artifacts used when developing or evolving systems. However, there is not much evidence about how practitioners actually work with artifacts and how well software tools support them. Therefore, we conducted an exploratory study based on 29 interviews with software practitioners to understand the current practice of presenting and manipulating artifacts in tools, how practitioners deal with the challenges encountered, and how these challenges affect the usability of the tools used. We found that practitioners typically work with several interrelated artifacts concurrently, less than half of these artifacts can be displayed entirely on a large screen, the artifact interrelationship information is often missing, and practitioners work collaboratively on artifacts without sufficient support. We identify the existing challenges of working with artifacts and discuss existing solutions proposed addressing them. Our results contribute to the body of knowledge about how practitioners work with artifacts when developing or evolving software, the challenges they are faced with, and the attempts to address these challenges.     

有效降低方块效应的DCT系数量化方法

方块效应是基于块的混合视频编码算法产生的重要特征失真,严重影响了压缩后视频的主观质量。分析产生方块效应的根本原因,在此基础上提出一种有效降低方块效应的DCT系数量化方法。该方法通过调整量化过程,减小量化误差在块边界处的变化,增强相邻块间量化误差的相关性,从而降低了压缩后视频的方块效应。实验结果表明该方法能够有效降低方块效应,改善视频帧的主观质量。另外,该算法计算复杂度较低,并且与视频压缩标准兼容,具有实用性。     

Super Artifacts: Personal Devices as Intrinsically Multifunctional,Meta-representational Artifacts with a Highly Variable Structure

The computer is one of the most complex artifacts ever built. Given its complexity, it can be described from many different points of view. The aim of this paper is to investigate the representational structure and multifunctionality of a particular subset of computers, namely personal devices (PCs, laptops, smartphones, tablets) from a user-centred perspective. The paper also discusses the concept of “cognitive task”, as recently employed in some definitions of cognitive artifacts, and investigates the metaphysical properties of such artifacts. From a representational point of view, the article introduces the concepts of artifactual meta-representation and of semi-transparency, two features that personal devices share with some cognitive and non-cognitive artifacts. Recognising the meta-representational nature of personal devices and of other cognitive artifacts, thus overcoming semi-transparency, is important for the understanding of why different artifacts offer us different cognitive affordances as well as different cognitive advantages. In this sense, it is not simply a theoretical achievement, but has some important practical consequences. In our highly technological world we can use different kinds of computers and artifacts for solving the same tasks, and we need to understand why some artifacts are better suited for some tasks than others. The ultimate characterisation of personal devices that emerges from this work is that of a sort of super-artifact. This special status is given to personal devices because of their distinctive features. They are in fact intrinsically multifunctional and meta-representational artifacts, with extremely variable structures. As super-artifacts, personal devices are characterised by macro-functionality and can be easily used as both cognitive artifacts and tools for other functions, depending on the kind of representations they instantiate.     

基于变分的CT图像环形伪影校正

有效去除CT图像中环形伪影是医学图像处理领域的一个重要研究方向,现有的方法在去除环形伪影的同时,对CT图像的边缘及细节保留存在困难和挑战.本文采用变分优化的思想,将环形伪影的去除问题建模为一个能量最小化问题,来缓解保持图像信息和去除伪影之间的矛盾,提出了一种后处理的伪影校正算法.根据环形伪影产生机理和特性表现构造有针对性的变分模型,一是从环形伪影的几何特性入手,设计更为合理的梯度保真形式,增强模型对图像细节信息的保护;二是从环形伪影的边缘特性入手,构建具有伪影辨识能力的相对全变分正则项,降低模型对图像结构性信息的影响.基于构造的变分模型,采用高效的优化求解算法,实现环形伪影的有效去除.对比实验表明,无论在视觉观察还是定量分析方面,本文算法均体现出了较好的性能.     

一种X射线射束硬化校正方法

在医学CT成像系统中,X射线源发出的X射线是多能的,若直接对其进行传统方法的重建,会导致射束硬化,使重建的图像出现“杯状”或“条状”伪影,降低图像质量,干扰诊断等问题,因此需要对CT硬化伪影进行校正。本文提出了一种射束硬化伪影的校正方法,利用近似材料衰减系数与x射线能量的关系,通过从原始重建图像中减去伪影图像来获得校正后的图像。该方法不需要先验知识。数值实验表明,该方法能有效、快速地校正射束硬化伪影。     

一种有效的块DCT编码图像去块效应算法

块离散余弦变换在低比特率时其恢复图像的块边界上会出现明显可见的方块效应,从而降低了图像的视觉质量。提出一种基于小波域特征分析的去块效应算法,目的是尽量消除块效应的同时充分保护图像的边缘信息。图像空域中的块效应,在小波域高频子带表现为平行线效应、竖直线效应或者网格效应。通过为各个子带设置自适应操作算子去抑制块边界系数能量聚集现象,使得空域中的块效应得到消除。仿真结果表明在不同比特率下,对不同类型的图像,新算法都能得到较好的去块效应效果,并且具有较高的运算速度。     

Compression Artifacts in Perceptual Audio Coding

Perceptual audio coding achieves a high compression ratio by exploiting the perceptual irrelevance and data redundancies. By using advanced and sophisticated signal processing methods, perceptual coding has generated artifacts that are quite different from the traditional distortions. A new audio technology becomes mature through the successful modeling, measuring, and control on the artifacts incurred from the technology. With the advance of new coding modules in advanced audio coding (AAC), spectral band replication (SBR), and parametric coding, the incurred artifacts are far more difficult to model, measure, and control than those caused by previous encoding systems like pulse code modulation. This paper models the audible artifacts through the time-frequency diagrams, considers the artifacts-susceptible music types, and analyzes the critical encoding technologies incurring these artifacts.     

利用块效应特征的JPEG图像盲取证研究进展

数字图像的盲取证技术由于不依赖任何预嵌入的信息来鉴别图像真实性和完整性的优势,正逐步成为数字媒体安全领域新的研究热点。由于JPEG图像是目前最流行的图像格式,并且块效应是JPEG图像与生俱来的本质特征,因此如何更加有效地利用块效应特征对JPEG图像的真伪进行盲取证研究具有非常重要的现实意义和应用价值。首先对目前国内外利用JPEG图像编码特性的盲取证方法进行归类分析;然后重点针对利用块效应特征的JPEG图像盲取证技术展开讨论,详细介绍并总结了基于块效应测度和基于块效应网格提取的两类盲取证算法的核心思想和局限性;最后提出了存在的问题及未来的研究方向。     

基于JPEG块效应频域特性的合成图像检测

JPEG图像在压缩过程中所产生的块效应在功率谱曲线上体现为周期性波峰,而篡改JPEG图像所造成块效应不一致将导致周期性波峰的衰弱或消除。利用上述原理,提出了一种基于JPEG块效应频域特性的合成图像检测算法。算法对待测图像进行去噪,提取包含块效应的噪声,对其进行重叠分块并求得每块的块效应度量值,依据该度量值检测并定位篡改区域。实验结果表明,相对于传统的基于块效应不一致的算法,能够更好地检测多种不同图像格式的合成和篡改区域较小等情况。     

适应视觉感知的块效应消除算法

针对编码图像出现块效应的现象,提出一种以人类视觉感知驱动的块效应消除算法。传统块效应消除算法经常会导致图像边缘丢失,为了解决这个问题,本文对块效应影响像素应用自适应滤波器和模糊推理技术。仿真结果表明算法可以获得比传统算法更好的效果。     

基于分形的水声图像目标探测

针对水声图像中人造物体的探测问题，给出了一种基于分形分析的方法，由于分形模型可以较好地模拟自然物体，而与人工物体存在较大差距，所以以其为主要特征可以准确地将人造物体从自然背景中探测出来。本文讨论了分维的提取方法，根据分形特征将水声图像标记为人造目标区域和非人造目标区域，并对一定噪声干扰下该方法的应用进行了研究，给出了相应的实验结果。实验结果表明，分形特征可以实现人造目标和自然物体的分类，并具有一定的抗噪声性，适宜对水声图像中的目标进行探测和识别。     

Not all digital venture ideas are created equal: Implications for venture creation processes

Digital ventures are formed around ideas that have digital artifacts at their core. We develop theory that explains how the composition of digital artifacts influences venture creation processes. First, we develop propositions that link differences in the embodiment and coupling of digital artifact components to tensions in venture creation process inputs, behaviors, and outputs. Second, we link compositional differences in digital artifacts to differences in venture creation process initiation, duration, and outcome. Our theorizing establishes a foundation for future research on digital artifacts within and beyond entrepreneurship contexts, and for future research on entrepreneurship within and beyond digital artifact contexts.     

一种面向软件配置管理制品的层次分类方法

配置管理工具（Configuration Management Tool,CMT）作为运维自动化的组成部分,是实现开发运维一体化（Development and Operations,DevOps）的重要支撑技术.当前互联网开源社区中存在数量众多CMT脚本制品,但是缺乏有效的层次分类管理,给快速检索和高效利用CMT脚本制品造成困难.针对该问题,提出一种面向CMT制品的基于在线非结构化描述文档分析的层次分类方法.该方法利用标签共现性关系（tag co-occurrence）建立层次类别体系,基于描述属性特征,实现对CMT制品的层次分类器;并使用混合的样本划分方式针对“数据倾斜”问题进行了改进.对超过11000例训练数据和1000例测试数据进行实验,结果表明,改进的样本划分方式得到的最佳查准率、查全率、调和平均值分别达到0.81、0.88、0.85,较传统方式查全率提高0.15,调和平均值提高0.06,该结果验证了层次分类方法的有效性.