Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment

Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.

Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features

Visual odometry is critical in visual simultaneous localization and mapping for robot navigation.However,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight.Herein,a new human visual attention mechanism for point-and-line stereo visual odometry,which is called point-line-weight-mechanism visual odometry(PLWM-VO),is proposed to describe scene features in a global and balanced manner.A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism,where sufficient attention is assigned to position-distinctive objects(sparse features in the environment).Furthermore,the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features.Compared with the state-of-the-art method(ORB-VO),PLWM-VO show a 36.79%reduction in the absolute trajectory error on the Kitti and Euroc datasets.Although the time consumption of PLWM-VO is higher than that of ORB-VO,online test results indicate that PLWM-VO satisfies the real-time demand.The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry,but also quantitatively demonstrates the superiority of the human visual attention mechanism.

Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics.To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed.The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

New algorithm for infrared small target image enhancement based on wavelet transform and human visual properties

The key to the wavelet based denoising teehniquea is how to manipulate the wavelet coefficients. By referring to the idea of Inclusive-OR in the design of circuits, this paper proposes a new algorithm called wavelet domain Inclusive-OR denoising algorithm（WDIDA）, which distinguishes the wavelet coefficients belonging to image or noise by considering their phases and modulus maxima simultaneously. Using this new algorithm, the denoising effects are improved and the computation time is reduced. Furthermore, in order to enhance the edges of the image but not magnify noise, a contrast nonlinear enhancing algorithm is presented according to human visual properties. Compared with traditional enhancing algorithms, the algorithm that we proposed has a better noise reducing performanee , preserving edges and improving the visual quality of images.

空间科学实验数据可视化分析系统技术研究

为提升空间科学实验数据的分析效率与利用水平,设计以数据可视化为切入点,支持多领域、长周期空间科学实验数据交互分析的系统架构,对系统关键技术进行研究。设计分析算法动态扩展和服务映射流程,支持数据分析算法的扩展与更新,建立算法的快速处理模式和复杂分析模式,兼容与支持不同类型分析算法的处理流程。通过多可视化组件之间的数据联动,直观且同步呈现相关联数据之间的相关性。采用可视化视图交互调整和布局,支持不同数据、不同显示屏幕的个性可视化需求。系统应用表明:系统已集成了十余种空间科学实验数据分析算法,实现了多领域空间科学实验数据的可视化分析。

基于快速局部对比度和目标特征的星图弱小目标检测算法

为了解决局部对比度方法在用于星图空间目标检测时存在运算量大和去除背景噪声困难的问题,提出了一种基于快速局部对比度和目标特征的方法来检测目标。在对比度计算前、对比度计算中和对比度计算后3个环节,分别提高了算法实时性、对复杂背景的抑制和去除噪声。首先,通过中值滤波去除高频噪声;然后,通过快速局部极大值滤波确定目标区域,通过局部对比度计算抑制背景,突出目标成像特征;最后,根据目标成像特征,设置目标能量分布、目标能量集中和目标能量传递3个特征函数,通过设置特征阈值去除噪声,提取真实目标。实验结果表明,本文所提方法在检测率和时间消耗上均具有优越性,对于信噪比为1.5的目标有95%的检测率,平均耗时仅为某些对比方法的1/30~1/6。本文所提方法更适用于星图复杂背景条件下的目标快速检测,满足星图空间目标检测算法鲁棒性强、实时性高的要求。

多模态医学图像融合图像质量评估

目前大多数的质量评估算法都应用于自然图像的融合场景,缺乏专用评价的医学图像数据集及多模态医学融合图像的质量评估算法。针对此问题,利用17种经典的医学图像融合方法构建医学图像主观数据集,解决无专用评价数据集的问题;提出一种基于色彩相似度(color similarity,CS)和信息相似度(information similarity,IS)的客观医学图像质量评价方法。将CS模块用于测量局部颜色失真,在传统的池化层上添加背景分离模块使其适用于医学图像多背景干扰特性;将IS模块用于衡量信息失真,改进图像熵的计算方法,添加过滤模块以剔除图像噪声。实验结果表明,所提方法的预测值和主观数据集客观评分具有更好的一致性,更符合人类视觉系统。

基于双层局部能量因子的红外小目标检测方法

红外小目标的检测一直是红外追踪系统的关键技术,针对现有红外小目标检测方法在复杂背景下易造成虚警、检测速度慢的不足,从人类视觉系统的角度出发,参考了多尺度局部能量因子检测方法(multiscale local contrast measure using a local energy factor,MLCM-LEF),提出了一种基于双层局部能量因子的红外小目标检测方法.从局部能量差异与局部亮度差异两个角度进行目标检测,使用双层局部能量因子从能量角度描述小目标与背景的相异程度,同时采取加权亮度差因子从亮度角度对图像进行目标检测,通过二维高斯融合上述二者的处理结果,最终利用图像均值和标准差进行自适应阈值分割,提取红外小目标.经过公开数据集实验测试,该方法在抑制背景噪声、减低虚警概率的表现上比主流的检测方法有所提升,与MLCM-LEF算法相比,基于双层局部能量因子的方法将单帧检测时间降低至三分之一.

Local field potentials,spiking activity,and receptive fields in human visual cortex

The concept of receptive field(RF) is central to sensory neuroscience. Neuronal RF properties have been substantially studied in animals,while those in humans remain nearly unexplored. Here, we measured neuronal RFs with intracranial local field potentials(LFPs) and spiking activity in human visual cortex(V1/V2/V3). We recorded LFPs via macro-contacts and discovered that RF sizes estimated from lowfrequency activity(LFA, 0.5–30 Hz) were larger than those estimated from low-gamma activity(LGA, 30–60 Hz) and high-gamma activity(HGA, 60–150 Hz). We then took a rare opportunity to record LFPs and spiking activity via microwires in V1 simultaneously. We found that RF sizes and temporal profiles measured from LGA and HGA closely matched those from spiking activity. In sum, this study reveals that spiking activity of neurons in human visual cortex could be well approximated by LGA and HGA in RF estimation and temporal profile measurement, implying the pivotal functions of LGA and HGA in early visual information processing.

基于Visual Foxpro 6.0的人事管理系统设计

根据企事业单位人力资源管理的需要,利用Visual Foxpro 6.0数据库应用软件,在对职工各方面情况进行详细分类的基础上,经过系统分析与设计,开发了某单位的人事管理系统;该系统可对人员信息进行采集、存储、加工、维护和使用,并通过信息录入、查询、打印、更新等功能的运用,实现人员信息的计算机化管理,提高信息的准确性和工作效率。

智能化信息系统对飞行员视觉搜索特性的影响

为探究飞行员使用智能化信息系统的视觉搜索特性,设计以信息系统等级(交互系统和电子手册)为自变量、以视觉搜索特性(视觉搜索特征和绩效水平)为因变量的对比实验,记录20名航线飞行员在模拟机实验场景中所关注的各人机交互界面的眼动数据,通过统计分析、构建视觉搜索绩效评估模型,反映飞行员视觉搜索特征及绩效水平。结果表明:两组飞行员的注视总持续时间、注视总持续时间占比、注视次数等视觉搜索特征存在显著差异,且随着信息系统智能化等级的提高,飞行员在单发失效和襟翼卡阻故障场景中的视觉搜索绩效水平有效提高。由此,验证了信息系统智能化程度的提高在复杂场景下能对飞行员的工作绩效产生正效应,可为驾驶舱人机交互界面的设计、优化及迭代提供理论参考,保证飞行安全。

No-Reference Image Quality Assessment Method Based on Visual Parameters

Recent studies on no-reference image quality assessment (NR-IQA) methods usually learn to evaluate the image quality by regressing from human subjective scores of the training samples. This study presented an NR-IQA method based on the basic image visual parameters without using human scored image databases in learning. We demonstrated that these features comprised the most basic characteristics for constructing an image and influencing the visual quality of an image. In this paper, the definitions, computational method, and relationships among these visual metrics were described. We subsequently proposed a no-reference assessment function, which was referred to as a visual parameter measurement index (VPMI), based on the integration of these visual metrics to assess image quality. It is established that the maximum of VPMI corresponds to the best quality of the color image. We verified this method using the popular assessment database—image quality assessment database (LIVE), and the results indicated that the proposed method matched better with the subjective assessment of human vision. Compared with other image quality assessment models, it is highly competitive. VPMI has low computational complexity, which makes it promising to implement in real-time image assessment systems.

Clustering the Stations of Bicycle Sharing System

Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade.Since the large number of bicycle stations distribute widely in the city,it is difficult to identify their unique attributes and characteristics directly.Oriented to the real bicycle hire dataset in Hangzhou,China,the clustering analysis for the bicycle stations based on the temporal flow data was carried out firstly.Then,based on the spatial distribution and temporal attributes of calculated clusters,visual diagram and map were used to vividly analyze the bicycle hire behavior related to station category and study the travel rules of citizens.The experimental results demonstrate the relation between human mobility,the time of day,day of week and the station location.

多终端联合下的远程人机交互仿真培训系统设计

针对气象雷达仿真培训系统丢包率较大、远程人机交互数据传输速率较低的问题,设计多终端联合下的远程人机交互仿真培训系统。通过多传感器终端联合数据采集模块采集并融合数据,依据用户指令、生成控制指令,经由伺服控制模块控制目标物运动轨迹,生成培训训练内容与故障信号。依据故障信号及测试资源,模拟部件故障,测试培训效果。利用人机交互界面呈现培训内容,通过视觉感知设计人机交互界面优化模型,利用遗传算法求解该模型,获取最优交互界面。实验结果证明,该系统可有效采集气象雷达数据,并有效融合采集的数据,提升人机交互仿真培训操作的熟练程度,最低资源交互输出幅值是82 dB,最低平均传输速率为3.82 MB/s。

结合纹理结构相似度和轮廓相似度的SAR干扰效果评估

对SAR干扰效果的评估是衡量抗干扰方法的有效性及SAR系统抗干扰性能的一项重要的综合性指标,文中提出一种结合图像纹理结构相似度和图像轮廓匹配度的SAR干扰效果评估方法(GSIM)。该方法利用图像轮廓匹配度和图像结构相似度构建图像干扰综合评估指标体系,根据指标在不同条件下的重要性进行不同权重的赋值,得到综合的评价结果。为了验证该方法的有效性及准确性,对两组实验图像进行了不同程度的噪声干扰和条纹干扰,通过GSSIM、LKS、GSIM三种评估方法对干扰后的两组实验图像的相似度数据进行对比分析,发现GSIM方法根据不同的干扰环境进行方法组合及权重分配能够对干扰效果进行客观评价,且评价结果更符合人眼的主观感受。该方法为SAR目标的伪装和防护提供了有效的理论支持和技术支撑。

基于熵掩蔽的DCT域恰可察觉失真模型

为提高离散余弦变换(discrete cosine transform,DCT)域恰可察觉失真(just noticeable distortion,JND)模型阈值精度并避免跨域操作,将熵掩蔽效应引入DCT域JND模型。首先,从自由能理论和贝叶斯推理出发,设计基于DCT域纹理能量相似性的自回归模型模拟视觉感知过程中的自发预测行为;其次,探索视觉感知与预测残差的映射关系得到块级无序度,并将熵掩蔽效应建模为关于无序度的JND阈值调节因子;最后,结合空间对比敏感度函数、亮度自适应掩蔽以及对比度掩蔽,提出基于熵掩蔽的DCT域JND模型。与现有DCT域JND模型相比,所提模型所有运算均在DCT域执行,更高效简洁。主观、客观实验结果表明,所提模型在感知质量相同或更好的情况下,噪声污染图的平均峰值信噪比(peak signal-to-noise ratio,PSNR)值比其他4个JND对比模型低2.04 dB,更符合人眼视觉系统的感知特性。

基于Gabor滤波的并行网络无参考图像质量评价

为解决在基于深度学习的无参考图像质量评价(no-reference image quality assessment,NR-IQA)领域内图片失真可能导致的图片边缘模糊问题,提出一种基于Gabor滤波的并行网络无参考图像质量评价算法。考虑到图片失真的非均匀性,利用可提取空间局部频域特征的Gabor滤波器来获取边缘图片;将灰度图片和边缘图片共同输入并行网络,融合并行网络所提取到的图像质量特征,并通过全连接层所组成的回归网络将其映射到质量分数。该算法采用128×128的图片块,既能达到增加数据集的作用,又不过多损失图片的整体信息。本文在LIVE和TID2013共2个图像数据集上进行了实验,并与其他性能良好的无参考图像质量评价算法进行了对比。实验结果表明,所提出的算法与人的视觉系统(human visual system,HVS)具有较高的一致性。

基于视障群体屏幕智能协助系统的研究与实现

互联网的迅猛发展和电子产品的高速普及给人们的生活带来许多便捷,视障人群却因色弱、色盲、失明、眼部老龄化等原因无法像正常人一样使用主流电子产品。通过对传统屏幕协助系统的研究和分析,提出了一系列优化方案,设计和实现了面向视障人群的屏幕智能协助系统,通过语音交互、自学习模式等实现无障碍化操作,提高其操作电子产品的效率和客户体验。

基于人眼视觉特性的交互界面图像增强仿真

交互界面图像会受到电路噪声、传输损耗等因素的影响,使图像出现较多噪声,导致交互界面图像效果较差,设计一个基于人眼视觉特性的交互界面图像增强仿真方法。进行图像转换与图像去噪处理,将人眼视觉特性应用到图像处理中,计算亮度敏感特性与图像空间频率,进行图像灰度处理和细节处理,并将彩色恢复因子引入到图像处理中,实现交互界面图像增强。实验结果表明,所研究的基于人眼视觉特性的交互界面图像增强方法有效提高了图像的平均梯度与信噪比,提高了图像增强效果,并且增强后的图像噪声、均方误差也较低,满足方法预期设计目标。

迷彩伪装目标检测的视觉特征偏好研究

迷彩伪装通过设计颜色和纹理图案来破坏目标的固有形状,其检测依赖的视觉特征应与常规目标不同。然而卷积神经网络的黑盒性质使得不同视觉特征对模型识别的贡献程度无法获知。为解决上述问题,借鉴人类视觉系统设计了一种适用于伪装场景的视觉特征解耦方法,解耦并分析目标检测模型在颜色、纹理和形状特征上的偏好程度。具体来说,使用消除单一特征并保留其余特征的解耦框架,以模型的性能下降情况作为偏向性的衡量标准,通过灰度化处理消除图像的颜色特征,使用区域置乱破坏目标的纹理特征,对目标轮廓取内接形状以改变目标的形状特征。在公开的迷彩伪装人员数据集和常规人员检测数据集上分别进行实验,结果显示,迷彩伪装目标的检测主要依赖纹理,常规目标的检测主要依赖形状。