模糊化在人脸识别中的作用研究

系统研究了在没有噪声的情况下对图像进行模糊处理对人脸图像识别效果的影响,从而提出了一种新的用于提高正确识别率的方法。     

图象模糊点扩散函数的求解

给出了一种求解图象模糊点扩散函数的实用方法,成像系统在远处和近处对同一景物所成的两幅图象,远处像 处像模糊,图象模糊点扩散函数描述了从远外像中求解远处像的转换关系,针对求解过程中的数学病态性,采用高斯函数约束枚举法进行正则化约束计算,实验表明,该方法具有抗噪声干扰能力强,运算速度快等优点。     

基于提升小波的运动模糊图像恢复

高频噪声对运动模糊图像的经典频域恢复方法影响较大,针对上述问题采用原方法存在运算量大的缺陷,提出了一种基于提升小波的运动模糊图像恢复方法:即对含噪运动模糊图像用9/7提升小波进行变换;对变换所得的低频包含运动模糊的部分用经典频域方法进行恢复,对变换所得的高频部分采用软域值自适应去噪处理;最后,对两部分处理结果进行9/7提升小波逆变换。仿真实验结果表明,方法能够较好的对运动模糊图像进行恢复。     

Histogram Preserving Image Transformations

Histograms are used to analyze and index images. They have been found experimentally to have low sensitivity to certain types of image morphisms, for example, viewpoint changes and object deformations. The precise effect of these image morphisms on the histogram, however, has not been studied. In this work we derive the complete class of local transformations that preserve or scale the magnitude of the histogram of all images. We also derive a more general class of local transformations that preserve the histogram relative to a particular image. To achieve this, the transformations are represented as solutions to families of vector fields acting on the image. The local effect of fixed points of the fields on the histograms is also analyzed. The analytical results are verified with several examples. We also discuss several applications and the significance of these transformations for histogram indexing.     

全变分极端通道先验的盲图像去噪去模糊

图像先验是图像复原中求解不适定问题的关键。针对在图像具有显著噪声时,极端通道先验去模糊算法容易产生振铃伪影和无法抑制噪声的缺点,利用全变分模型可以同时抑制噪声和保护边缘的优势,提出一种有效的全变分极端通道先验的盲图像去噪和去模糊模型。首先,将全变分模型分别引入暗通道和亮通道中,用于保护图像的边缘及消除噪声或振铃伪影;其次,利用半二次分裂技术解决模型的非凸问题和估计潜在的清晰图像;最后,用迭代多尺度盲反褶积估计图像的模糊核。实验结果表明,该算法能够在抑制噪声的同时很好地保护图像的边缘细节和消除振铃伪影。相比近几年具有代表性的其他方法,该模型的鲁棒性、主观视觉效果和客观评价指标均有明显提高。     

基于像素运动的图像模糊机理与恢复技术

从像素运动的角度分析了因运动造成图像模糊的实际过程,根据模糊图像像素间的关系建立了一维和二维匀速直线运动所造成的模糊图像的模糊模型,进而由模糊过程的逆过程给出了恢复模型。最后对匀速直线运动造成的图像模糊进行了模拟和恢复,取得了令人满意的效果。     

基于模糊函数估计和变分泛函数优化的含噪声模糊图像复原算法

针对摄影图像中常见的噪声和模糊同时退化图像的问题,提出一种Radon变换估计模糊函数结合变分泛函优化的图像复原算法。算法首先利用方向滤波器对图像去噪,然后利用Radon变换以去除方向滤波在模糊函数估计中引起的偏差,再利用模糊函数通过去卷积操作去除模糊,最后在去模糊的基础上利用 L0梯度范数的变换泛函的优化算法,去除剩余噪声从而对图像进行复原。实验表明,该算法可以有效地恢复摄影图像中的场景。     

位图的转向算法及其实现

多媒体编辑中图形的操作占据着重要的地位，尤其是位图的平面旋转和立体翻转更是其中的主要部分，也更具复杂性和多样化。本文给出其中的一种算法，并经Delphi5.0调试通过，仅供同行们参考。     

Extracting Group Transformations from Image Moments

Image distortion induced by the relative motion between an observer and the scene is an important cue for recovering the motion and the structure of the scene. It is known that the distortion in images can be described by transformation groups, such as Euclidean, affine, and projective groups. In this paper, we investigate how the moments of image curves are changed by group transformations, and we derive a relationship between the change in image moments and the invariant vector fields of the transformation groups. The results are used to formalize a method for extracting invariant vector fields of affine transformations from changes in the moments of orientation of curve segments in images. The method is applied to a realtime robot visual navigation task.     

基于模糊模式感知模块的场景文本图像超分辨率算法

现有的场景文本识别器容易受到模糊文本图像的困扰, 导致在实际应用中性能较差. 因此近年来研究人员提出了多种场景文本图像超分辨率模型作为场景文本识别的预处理器, 以提高输入图像的质量. 然而, 用于场景文本图像超分辨率任务的真实世界训练样本很难收集; 此外, 现有的场景文本图像超分辨率模型只学习将低分辨率(LR)文本图像转换为高分辨率(HR)文本图像, 而忽略了从HR到LR图像的模糊模式. 本文提出了模糊模式感知模块, 该模块从现有的真实世界HR-LR文本图像对中学习模糊模式, 并将其转移到其他HR图像中, 以生成具有不同退化程度的LR图像. 本文所提出的模糊模式感知模块可以为场景文本图像超分辨率模型生成大量的HR-LR图像对, 以弥补训练数据的不足, 从而显著提高性能. 实验结果表明, 当配备提出的模糊模式感知模块时, 场景文本图像超分辨率方法的性能可以进一步提高, 例如, SOTA方法TG在使用CRNN文本识别器进行评估时, 识别准确率提高了5.8%.     

基于广义运动模糊模型的前向运动模糊核

从光流场的角度出发,建立了一种广义运动模糊模型,并依据该模型推导出前向运动模糊核,为高速铁路前向运动视频图像去模糊奠定了理论基础.给出了理论分析后,设计了一种快速生成前向运动模糊核的方法,在这个过程中,解决了3个具体问题：快速的运动估计方法的解析解、平面场景朝向的快速估计方法的解析解、前向运动模糊核的数值生成方法.实验结果验证了该算法的正确性.     

Semi-supervised object recognition based on Connected Image Transformations

We present a novel semi-supervised classifier model based on paths between unlabeled and labeled data through a sequence of local pattern transformations. A reliable measure of path-length is proposed that combines a local dissimilarity measure between consecutive patters along a path with a global, connectivity-based metric. We apply this model to problems of object recognition, for which we propose a practical classification algorithm based on sequences of “Connected Image Transformations” (CIT). Experimental results on four popular image benchmarks demonstrate how the proposed CIT classifier outperforms state-of-the-art semi-supervised techniques. The results are particularly significant when only a very small number of labeled patterns is available: the proposed algorithm obtains a generalization error of 4.57% on the MNIST data set trained on 2000 randomly chosen patterns with only 10 labeled patterns per digit class.     

Regularity Properties of Distance Transformations in Image Analysis

A distance transformation gives for every set in the image plane a function which measures the distance to the set. Several different methods of measuring the distance are possible, and some have undesirable properties. It turns out that many important distances can be defined by infimal convolution. Using this operation we study semiregularity in the sense of Borgefors and give conditions on the distance which are equivalent to this property.     

一种能抵抗仿射变换的数字图像水印方案

本文提出了一种基于离散傅里叶变换(DFT)的数字图像水印方案，采用图像边缘作为模板，跟踪图像所经历的几何变形，再利用DFT的性质，使其能够抵抗仿射(缩放和旋转)攻击，且提取水印时不需要原始图像。文中对水印方案进行了较详细的介绍，并给出了仿真实验结果。     

数字图像的两类非线性变换及其周期性

研究了Fibonacci-Q变换及其逆变换的周期性,并证明了Fibonacci变换周期与Arnold变换周期的关系式,利用Fibonacci-Q变换Arnold变换之间的内在关系,定义了一种新的非线性变换－A－F变换,同时对其变换周期及其逆变换周期进行了研究,也给出了这两类变换周期的简洁算法。由于A－F变换和Fibonacci-Q变换与Arnold变换的周期性大相径庭,在实际应用中可达到更加安全保密的效果。     

一种基于三种变换的图像盲水印算法

变换域数字水印技术是当前的一个研究热点。将DWT和DCT两种变换结合,并把Amold变换引入到两种变换域数字水印系统中。提高数字水印的隐蔽性和稳健性。实验结果表明,该算法嵌入水印后不易被察觉,对于各种攻击具有较强的鲁棒性。     

基于多种变换的遥感图像新型融合方法

针对多光谱图像空间分辨率低这一特点,提出一种在PCA变换基础上,利用小波变换和高通滤波相结合的图像融合算法。实现了ETM+全色波段与ETM+多光谱波段图像的融合,并从空间纹理信息,光谱真实性两个方面进行定性和定量评价。研究表明,该融合算法产生的光谱失真较小,同时很大程度地保持了高分辨率全色波段的空间纹理细节信息,是一种较好的图像融合方法。     

数字图像置乱使用的若干变换周期问题的研究

论文分析了图像加密过程使用的若干变换,对这些变换的周期进行了估算,并指出了这些变换在使用过程中应注意的问题。结果表明能有效地降低还原图像时所需的计算复杂度。     

Multi-focus image fusion using Content Adaptive Blurring

Multi-focus image fusion has emerged as an important research area in information fusion. It aims at increasing the depth-of-field by extracting focused regions from multiple partially focused images, and merging them together to produce a composite image in which all objects are in focus. In this paper, a novel multi-focus image fusion algorithm is presented in which the task of detecting the focused regions is achieved using a Content Adaptive Blurring (CAB) algorithm. The proposed algorithm induces non-uniform blur in a multi-focus image depending on its underlying content. In particular, it analyzes the local image quality in a neighborhood and determines if the blur should be induced or not without losing image quality. In CAB, pixels belonging to the blur regions receive little or no blur at all, whereas the focused regions receive significant blur. Absolute difference of the original image and the CAB-blurred image yields initial segmentation map, which is further refined using morphological operators and graph-cut techniques to improve the segmentation accuracy. Quantitative and qualitative evaluations and comparisons with current state-of-the-art on two publicly available datasets demonstrate the strength of the proposed algorithm.