光刀中心自适应阈值提取法

在讨论了常用的各种光刀中心提取法的基础上,提出了一种光刀中心自知应阈值提取法。该方法结合了极值法、阈值法和重心法,避开了它们各自的缺点,消除了噪声,从而实现了光刀中心的精确定位。该方法具有稳定性好、精度高、速度快等特点。     

基于三维结构张量的立体图像质量客观评价方法

根据梯度结构张量能够表示图像结构信息的特点,提出了一种基于三维结构张量的立体图像客观质量评价方法。首先分别求取原始和失真的立体图像水平、垂直和视点方向的梯度信息,以及敏感区域,并构造出立体图像中每个像素的三维结构张量矩阵;然后,提取三维结构张量矩阵的特征值和特征向量信息;最后,根据特征值和特征向量预测得到立体图像质量的客观评价值。实验结果表明,采用本文提出的客观评价方法对立体图像测试库进行评价,总体评价的Pearson线性相关系数(PLCC)和Spearman等级相关系数(SROCC)值均在0.92左右,Kendall相关系数(KROCC)值接近0.80,均方根误差(RMSE)值均在6.00左右;与其他方法相比,本方法具有较高的预测精确性。     

基于三维结构张量的立体图像质量客观评价方法

根据梯度结构张量能够表示图像结构信息的特点, 提出了一种基于三维结构张 量的立体图像客观质量评价方法。首先分别求取原始和失真的立体图像水平、垂直和 视点方向的梯度信息,以及敏感区域,并构造出立体图像中每个像素的三维结构张量矩 阵;然后,提 取三维结构张量矩阵的特征值和特征向量信息;最后,根据特征值和特征向量预测得到立体 图像质量的客 观评价值。实验结果表明,采用本文提出的客观评价方法对立体图像测试库进行评价,总体 评价的Pearson 线性相关系数(PLCC)和Spearman等级相关系数(SROCC)值均在0.92左右,Kendall相关系数 (KROCC)值 接近0.80,均方根误差(RMSE)值均在6.00左右;与其他方法相 比,本方法具有较高的预测精确性。     

高光谱图像质量评价

高光谱遥感图像是一种三维立体图像,可反映地物的空间信息和光谱信息,其数据量庞大。本文在理解高光谱图像数据评价重要性的基础上,归纳了高光谱图像数据定量化评价的多种评估参数,并提出了选择评估参数的指导原则,即根据高光谱图像数据应用处理的各个不同阶段的特点,选择合适的参数。     

YAG接触式光刀输出功率和效率的测量

应用液体量热法测量了二种白宝石（Ａｌ２Ｏ３单晶）ＹＡＧ接触式激光手术刀的输出功率和效率。     

基于三维感知的立体虚拟视点图像质量评价方法

为了评价立体虚拟视点图像的质量,提出了一种基 于三维感知的客观评价方法。综合考虑了立体虚拟视点图像两大最主要失真类型:单视点绘 制失真和立体视点不匹配失真。针对单视点绘制失真,先提取 当前视点失真图与无失真图的差异性区域,再针对该差异性区域计算平均结构相似度(MSSIM ),最后将左 右视点平均池化作为单目纹理特征值;针对立体视点不匹配失真,先对左右视点失真图分别 进行视差映射, 再提取映射图与该视点失真图的差异区域作为双目不匹配区域,然后针对不匹配区域计算MS SIM 值,最 后将左右视点平均池化作为双目竞争特征值;最终将两个特征值幂次融合,作为最终的立体 虚拟视点图像 质量评价客观指标。实验结果表明本方法有效匹配主观打分的DMOS值,皮尔森线性相关系数 和斯皮尔曼 秩相关系数分别为0.911和0.900,正确反映了 立体虚拟视点图像质量。     

采用激光光刀的叶片三维面形测量方法

提出一种采用激光光刀的叶片三维面形测量方法。介绍了方法原理、系统结构、信息获取与处理方案,并给出了对实际飞机发动机叶片的面形测量结果。     

图像质量评价方法研究进展

图像质量评价是图像处理领域的研究热点。该文综合论述了图像质量的主观和客观评价方法,重点阐述了单视点图像质量的客观评价方法。对目前比较常用的峰值信噪比和均方误差全参考评价算法进行了分析并指出其存在的问题。然后,对基于误差敏感度和基于结构相似度的评价算法进行了论述和分析,并对质降和无参考评价方法进行了综述。根据视点的个数,图像质量评价可分为对传统单视点图像和立体图像的评价。该文还对立体图像质量评价算法进行了分析讨论。最后,就图像质量评价算法的进一步发展提出了若干技术与研究方向的展望。     

数字电视图像质量的主观评价和信噪比的测量

本文介绍了数字电视图像质量的主观评价和信噪比的测量。     

Objective image quality assessment of 3D synthesized views

Depth-Image-Based-Rendering (DIBR) techniques are essential for three-dimensional (3D) video applications such as 3D Television (3DTV) and Free-Viewpoint Video. However, this process is based on 3D warping and can induce serious distortions whose impact on the perceived quality is far different from the one experienced in the 2D imaging processes. Since quality evaluation of DIBR-synthesized views is fundamental for the design of perceptually friendly 3D video systems, an appropriate objective quality metric targeting the assessment of DIBR-synthesized views is momentous. Most of the 2D objective quality metrics fail in assessing the visual quality of DIBR-synthesized views because they have not been conceived for addressing the specificities of DIBR-related distortions. In this paper, a new full-reference objective quality metric, 3DSwIM (3D Synthesized view Image Quality Metric), dedicated to artifacts detection in DIBR-synthesized view-points is presented. The proposed scheme relies on a comparison of statistical features of wavelet subbands of two input images: the original image and the DIBR-based synthesized image. A registration step is included before the comparison step so that best matching blocks are always compared to ensure “shifting-resilience”. In addition, a skin detection step weights the final quality score in order to penalize distorted blocks containing “skin-pixels” based on the assumption that a human observer is most sensitive to impairments affecting human subjects. Experimental tests show that the proposed method outperforms the conventional 2D and DIBR-dedicated quality metrics under test.     

Toward an unsupervised blind stereoscopic 3D image quality assessment using joint spatial and frequency representations

Existing blind stereoscopic 3D (S3D) image quality assessment (IQA) metrics usually require supervised learning methods to predict S3D image quality, which limits their applicability in practice. In this paper, we propose an unsupervised blind S3D IQA metric that utilizes the joint spatial and frequency representations of visual perception. The metric proposed in this work was inspired by the binocular visual mechanism; furthermore, it is unsupervised and does not require subject-rated samples for training. To be more specific, first, the various binocular quality-aware features in spatial and frequency domains are extracted from the monocular and cyclopean views of natural S3D image patches. Subsequently, these features are utilized to establish a pristine multivariate Gaussian (MVG) model to characterize natural S3D image regularities. Finally, with the learned MVG model, the final quality score for a distorted S3D image can be yielded using a Bhattacharyya-like distance. Our experimental results illustrate that, compared to related existing metrics, the devised metric achieves competitive prediction performance.     

图像处理中的质量评价简析

在图像处理中,图像质量的评价是一个重要的环节。比较全面系统的介绍和分析了当前常用的关于图像质量评价的几种方法,以及它们的原理、功效、优缺点和应用范围。     

Dual-branch vision transformer for blind image quality assessment

Blind image quality assessment (BIQA) has always been a challenging problem due to the absence of reference images. In this paper, we propose a novel dual-branch vision transformer for BIQA, which simultaneously considers both local distortions and global semantic information. It first extracts dual-scale features from the backbone network, and then each scale feature is fed into one of the transformer encoder branches as a local feature embedding to consider the scale-variant local distortions. Each transformer branch obtains the context of global image distortion as well as the local distortion by adopting content-aware embedding. Finally, the outputs of the dual-branch vision transformer are combined by using multiple feed-forward blocks to predict the image quality scores effectively. Experimental results demonstrate that the proposed BIQA method outperforms the conventional methods on the six public BIQA datasets.     

脚型三维曲面测量技术

提出了一种新的三维曲面测量方法,利用激光片光对脚进行光切,在脚的某一个截面上形成封闭光带,用3个CCD摄像机对截面光带成像,可一次获得脚的某一个截面的二维轮廓信息,再沿光切面的垂直方向步进测量,经CCD摄像机像面和光切平面之间的空间映射变换后可得到脚的整个三维曲面信息。     

A no-reference perceptual image quality assessment database for learned image codecs

The drastic growth of research in image compression, especially deep learning-based image compression techniques, poses new challenges to objective image quality assessment (IQA). Typical artifacts encountered in the emerging image codecs are significantly different from that produced by traditional block-based codecs, leading to inapplicability of the existing objective IQA algorithms. Towards advancing the development of objective IQA algorithms for recent compression artifacts, we built a learning-based compressed image quality assessment (LCIQA) database involving traditional block-based image codecs, hybrid neural network based image codecs, convolutional neural network based and generative adversarial network (GAN) based end-to-end optimized image coding approaches. Our study confirms the statistical difference and human perception difference between reconstructions of learned compression and traditional block-based compression. We propose a two-step deep learning model for learning-based compressed image quality assessment. Extensive experiments on LCIQA database demonstrate that our proposed model performs better than other counterparts on learning-based compressed images, especially on GAN compressed images, and achieves competitive performance to the state-of-the-art IQA metrics on traditional compressed images.     

No-reference image quality assessment using structural activity

Presuming that human visual perception is highly sensitive to the structural information in a scene, we propose the concept of structural activity (SA) together with a model of SA indicator in a new framework for no-reference (NR) image quality assessment (QA) in this study. The proposed framework estimates image quality based on the quantification of the SA information of different visual significance. We propose some alternative implementations of SA indicator in this paper as examples to demonstrate the effectiveness of the SA-motivated framework. Comprehensive testing demonstrates that the model of SA indicator exhibits satisfactory performance in comparison with subjective quality scores as well as representative full-reference (FR) image quality measures.     

一种改进的三坐标雷达测高方法

针对三坐标雷达双波束测高精度低及高度突跳问题,提出四波束测高方法,该方法在满足信号处理能力的情况下,采用四波束测量,利用回波幅度较大的两个波束进行仰角测量。用雷达设备进行实际测飞实验,测飞结果表明,该方法可以有效地提高测高精度,改善高度突跳现象。     

基于图像处理的激光双三角法测量三维曲面

分析了激光三角法在3D曲面测量系统中的非线性误差,及影响测量精度的各方面因素,提出了采用双摄像头的光学系统,利用基于神经网络的多维拟和标定的方法,较好地消除各种非线性系统误差和测量盲点问题,并且大大提高了测量精度。     

图像质量评价的研究现状与发展

目前互联网应用与多媒体通信已成为信息世界的主流,数字图像在获取、压缩编码、存储或传输过程中存在不同程度的退化而影响视觉效果,因此图像质量的评价具有重要的理论和实际意义.梳理了目前国内外图像质量评价的最新研究成果,并对其进行归类、分析、研究与评述.在此基础上,提出图像质量评价的发展方向与研究展望.