利用相位一致性的图像质量评价方法

各种图像处理建立在有效性地提取图像特征之上,如图像分类、分割和图像质量评价等,因此获取有效的图像特征对于图像处理意义十分重大。不同于在图像灰度的突变点处直接定义图像特征,相位一致性(PC)在傅里叶分量的相位保持高度一致的位置观测图像特征,获得了丰富的特征信息和精确的特征定位,与人类视觉系统(HVS)对图像特征的认知相符。提出了一种新的基于相位一致性特征的图像质量评价方法。该方法使用退化与参考图像的相位一致性在局部区域的相似度来测量图像质量的退化程度;并且考虑到相位一致性是纹理和边缘的反应,而人类视觉系统对纹理丰富的区域较为敏感,利用相位一致的局部最值作为加权值,将局部的相似度结合为单个的图像质量评分值。实验结果表明,提出的图像质量指标具有较好的主客观一致性。尤为重要的是,该指标对图像的亮度和对比度变化不敏感。     

基于Hilbert滤波器对的相位一致性边缘检测方法

基于梯度的边缘检测算法，主要是针对阶跃形边缘的检测。Morrone等人提出的基于局部能量和相位一致性的边缘检测方法，则同时适用于阶跃形和屋脊形两类边缘的检测。根据等Q分解原则设计了一组基于Hilbert变换的正交滤波器对，并根据广义相位理论提取滤波后的局部能量和相位信息，在分析了噪声影响和展频处理后，通过计算相位一致性检测图像中的边缘。由于基于相位一致性的边缘检测具有不受光照条件影响，无需计算局部能量极值的特点，因此，在边缘检测的准确率与定位精度上，取得了较好的试验效果。     

基于分块相位一致性的人脸识别算法

针对基于可见光的人脸图像的识别容易受光照和表情变化的影响,人脸的表情变化仅限于局部等问题,以及图像的相位一致性特征不受图像的亮度或对比度影响的特点,提出了一种基于分块相位一致性的人脸识别算法。该算法用log-gabor滤波器对图像进行滤波,利用相位一致性模型提取相位一致性特征图像;对每幅特征图像进行分块主元分析(PCA)处理;融合所有子图像的距离信息,采用最近邻分类器进行分类识别。实验证明该方法具有更好的识别性能。     

相位信息在电绝缘系统参数测量中的应用

介绍了相位一致性的理论基础及推导过程,引入相位一致性的近似模型:局部能量模型,将基于相位信息的边缘检测方法运用到电绝缘系统的边缘检测环节中,并最终输出测量结果.分析测量数据可知,利用相位一致性算法进行边缘检测,其边缘线条更加光滑细腻且封闭性良好,参数测量精度更高,能够比较方便地解决电缆绝缘层参数的测量问题.     

基于相位一致性改进的步态识别方法

为克服步态轮廓变化对步态识别的不利影响,采用步态能量图改进对数Gabor相位一致性特征,提出一种新的步态识别方法。利用局部能量计算方法及频率扩展与噪声补偿策略,使提取的步态特征更具识别性和定位性,并对该步态特征进行线性判别分析降维。应用基于欧氏距离的最近邻分类器在CASIA和USF步态数据库上进行测试,结果表明该方法在个体携包行走、穿着和视角变化的情况下均能较好地识别步态轮廓,相比现有步态识别方法具有更高的正确识别率。     

基于方向相位一致性的多源光学遥感图像匹配方法北大核心CSCD

针对多源遥感图像间往往具有较大的辐射差异和几何畸变等问题,提出了一种基于相位一致性模型和方向相位一致性特征的匹配方法。首先,利用相位一致性模型提取边缘图像进行分块相位相关,记录脉冲峰位置从而得到图像间的空间几何约束;然后,利用方向相位一致性描述模板区域特征,该特征具有良好的抗辐射畸变能力;最后,根据几何约束条件预测同名点位置,进行特征匹配。实验结果表明,该方法能够克服异源图像间较大的几何误差和非线性辐射差异,实现图像间的自动匹配。     

基于相位一致性图像处理的岩体裂隙自动检测

提出了一种基于相位一致性检测和分段线迭代连接并去除伪边缘的岩体裂隙自动检测方法。先对原始图像进行相位一致性边缘检测并细化处理,得到包含裂隙特征和伪边缘的分段线,再根据距离和角度原则对分段线迭代连接,裂隙分段线相互连接而不断完整的同时滤除不能连接而变长的伪边缘。实验表明,对于大体积岩体露头面图像的裂隙检测,该方法具有较高的精确性。     

基于整体变分的相位恢复

相位恢复是指利用直接测量得到的强度分布恢复相位从而重建波函数。为了能够在已知强度信息的情况下最大限度地恢复相位,结合强度传输方程提出一种基于整体变分的相位恢复算法：首先在一致性照明的情况下建立相位恢复模型,然后把相位恢复问题转化为带有整体变分正则化项的图像能量泛函极值问题,再使用有限差分牛顿法求出相位的最优解。该算法只需要测量临近空间平面上的光波的空间强度即可从强度图像中恢复相位信息,避免了由于干涉法要求光源的空间和时间连续所造成的分辨率、敏感性等问题。实验表明在恢复相位的同时可以保持良好的边缘。     

相位一致性指导的全参考全景图像质量评价

目的 全景图像的质量评价和传输、处理过程并不是在同一个空间进行的,传统的评价算法无法准确地反映用户在观察球面场景时产生的真实感受,针对观察空间与处理空间不一致的问题,本文提出一种基于相位一致性的全参考全景图像质量评价模型。方法 将平面图像进行全景加权,使得平面上的特征能准确反映球面空间质量畸变。采用相位一致性互信息的相似度获取参考图像和失真图像的结构相似度。接着,利用相位一致性局部熵的相似度反映参考图像和失真图像的纹理相似度。将两部分相似度融合可得全景图像的客观质量分数。结果 实验在全景质量评价数据集OIQA（omnidirectional image quality assessment）上进行,在原始图像中引入4种不同类型的失真,将提出的算法与6种主流算法进行性能对比,比较了基于相位信息的一致性互信息和一致性局部熵,以及评价标准依据4项指标。实验结果表明,相比于现有的6种全景图像质量评估算法,该算法在PLCC（Pearson linear correlation coefficient）和SRCC（Spearman rank order correlation coefficient）指标上比WS-SSIM（weighted-to-spherically-uniform structural similarity）算法高出0.4左右,并且在RMSE（root of mean square error）上低0.9左右,4项指标最优,能够获得更好的拟合效果。结论 本文算法解决了观察空间和映射空间不一致的问题,并且融合了基于人眼感知的多尺度互信息相似度和局部熵相似度,获得与人眼感知更为一致的客观分数,评价效果更为准确,更加符合人眼视觉特征。     

结合相位一致与全变差模型的高分辨率遥感图像边缘检测

目的边缘检测是有效利用遥感数据开展地物目标自动识别的重要步骤。高分辨率遥感图像地物类型复杂,细节信息过于丰富,使得基于相位一致的边缘检测结果中存在过多的噪声与伪边缘。为此提出了一种结合相位一致与全变差模型的高分辨率遥感图像边缘检测方法。方法根据相位一致原理,应用Log Gabor构造的2维相位一致模型,引入全变差去噪模型对基于相位一致的边缘强度图进行改进。结果借助有界变差空间对图像光滑性的约束,实现了高分辨率遥感图像噪声去除与伪边缘抑制,利用改进后的相位一致边缘强度图可有效检测高分辨率遥感图像的边缘。结论实验结果表明,与相位一致模型、Canny算法相比,该方法能消除了高分辨率遥感图像中同类地物内部细节特征形成的噪声,抑制相位一致边缘检测结果中的伪边缘,突出地物的真实边缘,并能正确地提取地物目标的整体轮廓信息,有助于后续地物目标的自动识别。     

Image feature detection from phase congruency based on two-dimensional Hilbert transform

The theory of phase congruency is that features such as step edges, roofs, and deltas always reach the maximum phase of image harmonic components. We propose a modified algorithm of phase congruency to detect image features based on two-dimensional (2-D) discrete Hilbert transform. Windowing technique is introduced to locate image features in the algorithm. Local energy is obtained by convoluting original image with two operators of removing direct current (DC) component over current window and 2-D Hilbert transform, respectively. Then, local energy is divided with the sum of Fourier amplitude of current window to retrieve the value of phase congruency. Meanwhile, we add the DC component of current window on original image to the denominator of phase congruency model to reduce the noise. Finally, the proposed algorithm is compared with some existing algorithm in systematical way. The experimental results of images in Berkeley Segmentation Dataset (BSDS) and remotely sensed images show that this algorithm is readily to detect image features.     

The Monogenic Scale-Space: A Unifying Approach to Phase-Based Image Processing in Scale-Space

In this paper we address the topics of scale-space and phase-based image processing in a unifying framework. In contrast to the common opinion, the Gaussian kernel is not the unique choice for a linear scale-space. Instead, we chose the Poisson kernel since it is closely related to the monogenic signal, a 2D generalization of the analytic signal, where the Riesz transform replaces the Hilbert transform. The Riesz transform itself yields the flux of the Poisson scale-space and the combination of flux and scale-space, the monogenic scale-space, provides the local features phase-vector and attenuation in scale-space. Under certain assumptions, the latter two again form a monogenic scale-space which gives deeper insight to low-level image processing. In particular, we discuss edge detection by a new approach to phase congruency and its relation to amplitude based methods, reconstruction from local amplitude and local phase, and the evaluation of the local frequency.     

Examplar coherent 3D face reconstruction from forensic mugshot database

Reconstructing 3D face models from 2D face images is usually done by using a single reference 3D face model or some gender/ethnicity specific 3D face models. However, different persons, even those of the same gender or ethnicity, usually have significantly different faces in terms of their overall appearance, which forms the base of person recognition via faces. Consequently, existing 3D reference model based methods have limited capability of reconstructing precise 3D face models for a large variety of persons. In this paper, we propose to explore a reservoir of diverse reference models for 3D face reconstruction from forensic mugshot face images, where facial examplars coherent with the input determine the final shape estimation. Specifically, our 3D face reconstruction is formulated as an energy minimization problem with: 1) shading constraint from multiple input face images, 2) distortion and self-occlusion based color consistency between different views, and 3) depth uncertainty based smoothness constraint on adjacent pixels. The proposed energy is minimized in a coarse to fine way, where the shape refinement step is done by using a multi-label segmentation algorithm. Experimental results on challenging datasets demonstrate that the proposed algorithm is capable of recovering high quality 3D face models. We also show that our reconstructed models successfully boost face recognition accuracy.     

Learning 3D Deformation of Animals from 2D Images

Understanding how an animal can deform and articulate is essential for a realistic modification of its 3D model. In this paper, we show that such information can be learned from user‐clicked 2D images and a template 3D model of the target animal. We present a volumetric deformation framework that produces a set of new 3D models by deforming a template 3D model according to a set of user‐clicked images. Our framework is based on a novel locally‐bounded deformation energy, where every local region has its own stiffness value that bounds how much distortion is allowed at that location. We jointly learn the local stiffness bounds as we deform the template 3D mesh to match each user‐clicked image. We show that this seemingly complex task can be solved as a sequence of convex optimization problems. We demonstrate the effectiveness of our approach on cats and horses, which are highly deformable and articulated animals. Our framework produces new 3D models of animals that are significantly more plausible than methods without learned stiffness.     

Surface extraction from multi-material components for metrology using dual energy CT

This paper describes a novel method for creating surface models of multi-material components using dual energy computed tomography (DECT). The application scenario is metrology and dimensional measurement in industrial high resolution 3D x-ray computed tomography (3DCT). Based on the dual source / dual exposure technology this method employs 3DCT scans of a high precision micro-focus and a high energy macro-focus x-ray source. The presented work makes use of the advantages of dual x-ray exposure technology in order to facilitate dimensional measurements of multi-material components with high density material within low density material. We propose a workflow which uses image fusion and local surface extraction techniques: a prefiltering step reduces noise inherent in the data. For image fusion the datasets have to be registered. In the fusion step the benefits of both scans are combined. The structure of the specimen is taken from the low precision, blurry, high energy dataset while the sharp edges are adopted and fused into the resulting image from the high precision, crisp, low energy dataset. In the final step a reliable surface model is extracted from the fused dataset using a local adaptive technique. The major contribution of this paper is the development of a specific workflow for dimensional measurements of multi-material industrial components, which takes two x-ray CT datasets with complementary strengths and weaknesses into account. The performance of the workflow is discussed using a test specimen as well as two real world industrial parts. As result, a significant improvement in overall measurement precision, surface geometry and mean deviation to reference measurement compared to single exposure scans was facilitated.     

Sparse phase imaging based on complex domain nonlocal BM3D techniques

The paper is addressed to 2D phase and amplitude estimation of complex-valued signals – that is, in particular, to estimation of modulo-2π interferometric phase images from periodic and noisy observations. These degradation mechanisms make phase image estimation a challenging problem. A sparse nonlocal data-adaptive imaging formalized in complex domain is used for phase and amplitude image reconstruction. Following the procedure of patch-based technique, the image is partitioned into small overlapping square patches. Block Matching Three Dimensional (BM3D) technique is developed for forming complex domain sparse spectral representations of complex-valued data. High Order Singular Value Decomposition (HOSVD) applied to BM3D groups enables the design of the orthonormal complex domain 3D transforms which are data adaptive and different for each BM3Ds group. An iterative version of the complex domain BM3D is designed from variational formulation of the problem. The convergence of this algorithm is shown. The effectiveness of the new sparse coding based algorithms is illustrated in simulation experiments where they demonstrate the state-of-the-art performance.     

粪便镜检图像中红白细胞的分割与识别方法

针对粪便镜检图像中具有弱边界的红、白细胞的识别问题,研究了基于Chan-Vese模型的兼顾邻域区域边缘和纹理综合信息的分割方法。用八向Sobel弥补透明细胞的模糊边缘,通过细胞域内纹理和边缘信息互补而采用兼顾全局和局部能量分布的Chan-Vese模型的分割方法,并采用具备更好的数据泛化作用的随机决策森林进行分类。实验证明,提出的兼顾边界与域内纹理的改进型Chan-Vese分割方法使粪便镜检图像中红、白细胞的分割精度达到了95.3%。该方法对粪便镜检图像中的有形物体具备更高的分辨能力和光学环境适应性。     

基于边缘熵和局部FT分布的超声图像分割模型

由于采用高斯和瑞利分布描述超声图像均存在较大偏差,且分割过程缺乏超声图像边缘信息引导,致使其相应的局部高斯分布拟合(LGDF)模型和局部瑞利分布拟合(LRDF)模型对超声图像分割性能不理想.针对上述问题,提出了一种边缘熵加权的局部Fisher-Tippett(FT)分布拟合模型.该模型根据超声图像中目标和背景在局部区域...