关于变换编码图像块效应后处理方法的研究

基于H.Paek等人提出的自适应后处理方法DAPT（DCT－based Adaptive Post-processing Technique）和图像像素梯度的变化特性，提出了一种快速块效应后处理方法。通过对JPEG解码图像处理实验的结果表明：新方法在保持DAPT处理的图像质量条件下，减少DCT（Discrete Consine Transform）运算次数一半以上，大大地提高了解码图像块效应的处理速度。     

实时监控系统中运动人体图像分割

在运用差图像算法进行运动人体检测、噪声消除的基础上，提出了将统计直方图与梯度边缘检测相结合的算法以消除运动人体的影子；同时运用图像扩展模板并配合图像边界处平滑的算法对被分割人体的断裂处进行连接填充。实验结果验证了上述算法对噪声抑制、人影消除以及人体图像断裂处填充的有效性。     

一种自适应的图像双边滤波方法

提出一种利用双边滤波的图像平滑滤波方法,即在滤除图像中高频噪声的同时,按照图像亮度变化保持图像中处于高频部分的边缘信息的自适应滤波过程。该滤波方法将传统的Gauss滤波器的权系数优化成Gauss函数和图像的亮度信息乘积的形式,优化后的权系数再与图像作卷积运算。这样,滤波时就可以考虑到图像的亮度信息,在滤除图像噪声的同时尽量保持了图像的边缘。由于双边滤波的方法可以使滤波器的权系数随着图像的亮度变化而改变,所以在滤波过程中能达到自适应滤波的目的。     

图像噪声方差大小的测量新算法

针对图像中的高斯白噪声,提出了一种利用小波系数分布特征进行噪声估计的算法。根据小波系数的分布特点,利用广义高斯分布模型对小波系数进行拟合,并用广义高斯分布模型参数（尺度和形状参数）提出了一种有效的噪声方差估计算法。实验结果表明,提出的噪声方差估计算法能有效地估计噪声方差大小,同时估计的结果要好于其它相关的算法。这种噪声测量算法具有高精度性和快速性。     

一种自适应二值化的毫米波图像边缘检测方法

针对毫米波图像分辨率较低、噪声大、成像物体边缘提取困难等问题,提出了一种基于自适应图像二值化的毫米波图像边缘检测方法。该方法通过对图像直方图的分析,自动地确定两个峰值的位置,得出阈值,对图像进行二值化,再对二值化后的图像进行边缘检测。实验表明,这种方法能较好地检测出毫米波图像中人与物体的边缘信息,同时又去掉了大多数由噪声引起的伪边缘。该方法避免了大量的方差计算,计算量仅为大津法的 50%左右,能够满足自适应性和实时性的要求。     

基于光照调整和特征曲线的全景图拼接

图像拼接是生成全景图的关键技术之一。针对传统图像拼接方法只能处理光照一致图像的问题,笔者提出了一种对环境光照鲁棒的全景图拼接方法。该方法运用同态滤波、特征点自动提取、特征曲线匹配等技术,有效地解决了图像拼接中的光照差、区域精确匹配、噪声点及图像融合问题。实验表明对光照变化的图像采用该方法进行拼接可以获得满意的视觉效果。     

基于分割区域的SAR图像配准方法研究

合成孔径雷达(Synthetic Aperture Radar,简称SAR)图像受到斑点噪声影响以及成像条件的变化,使得同一场景的两幅SAR图像之间存在很大差异,利用基于边缘特征或灰度信息进行SAR图像配准,难以达到预期效果.基于此,本文提出一种基于分割区域的SAR图像配准方法,该方法首先利用主动轮廓方法对去噪后的SA...     

一种基于空间自适应的盲图像复原算法

针对非负有限支持域约束递归逆滤波（NAS-RIF）图像盲复原算法对噪声敏感问题，提出一种基于空间自适应的改进算法。利用最优阈值估计图像支持域，在代价函数中引入空间自适应加权因子和尺度因子，结合正则化技术对图像复原进行局部控制，并对不同信噪比和背景的图像进行了实验。结果表明，在较低信噪比情况下，改进NAS—RIF算法仍具有较好的复原效果，有效地达到了保存图像细节并抑制噪声的目的。     

基于梯度的自适应椒盐噪声滤波算法

针对传统中值滤波方法不能有效保持图像细节信息和对图像适应能力差的问题,提出一种改进的椒盐噪声滤除算法。算法基于先检测、后滤波的思想,根据图像的极小梯度矩阵自适应计算噪声阈值,提高了噪声检测的准确性;为了更好地保持图像细节,对检测出的噪声像素进行多窗口中值滤波。多组去噪实验表明：所提算法对污染程度不同的图像具有良好的适应性,在滤除噪声的同时还可以有效还原图像边缘等细节信息。     

红外图像边缘检测的循环移位算法

提出一种基于Bezier曲线的红外图像边缘检测的循环移位算法。为抑制红外图像中的噪声的影响,利用Bezier曲线法平滑图像灰度直方图中的噪声,得到Bezier直方图。利用提出的“循环移位法”探测Bezier直方图曲率曲线的极大和极小值,确定分割阈值和量化灰度值,实现对原始红外图像的分割,对分割后的图像进行边缘检测。实验结果表明,新算法简单有效,在准确检测红外图像边缘信息的同时又能抑制图像中噪声的影响,定位精度高,而且能够得到单像素边缘,在性能上优于传统的几种边缘检测算子。     

面向自动对焦的图像预处理技术

从噪声、亮度一致性及视场变化等角度分析了在对焦系统中进行图像预处理的必要性。提出维纳滤波与亮度归一化相结合的图像预处理技术,去除固定模式噪声和亮度变化的影响。实验表明,经过预处理后明显改善了对焦评价函数曲线的形状:曲线更加平滑,局部峰值得以去除,同时曲线灵敏度提高78%。所提出的预处理技术使对焦搜索避免陷入局部极值,在提高对焦速度与对焦精度两方面均有应用价值。     

指纹图像预处理方法的研究

针对指纹图像降质带来的困难，提出基于指纹结构特征的某些合理假设，在此基础上设计要应的处理算法，提出了增强指纹图像对比度、提取指纹有效区域、根据方向信息分割图像以及去除图像中气泡噪声的算法等。实施证明，这些算法处理效果好、运行速度快、编程简单，有效地解决了降质图像的预处理问题。     

用数学形态学方法进行红外图像分层解理

将形态学算子作用于高噪声,低质量的红外图像,对之进行分层解理还原,即将原本由各种信号叠加而成的红外图像重新分解成器件噪声,工作噪声和目标,背景等不同层次的信号。这种图像解理方法,以几何论和集合运算为基础,因而原理直观,计算简单,能为红外目标识别与跟踪提供有益的预处理。     

Fast and Accurate Cell Tracking by a Novel Optical-Digital Hybrid Method

An innovative methodology to detect and track cells using microscope images enhanced by optical cross-correlation techniques is proposed in this paper. In order to increase the tracking sensibility, image pre-processing has been implemented as a morphological operator on the microscope image. Results show that the pre-processing process allows for additional frames of cell tracking, therefore increasing its robustness. The proposed methodology can be used in analyzing different problems such as mitosis, cell collisions, and cell overlapping, ultimately designed to identify and treat illnesses and malignancies.     

Improved quadtree image segmentation approach to region information

Images are full of information and most often, little information is desired for subsequent processing. Hence, region of interest has key importance in image processing. Quadtree image segmentation has been widely used in many image processing applications to locate the region of interest for further processing. There are also variable block-size image coding techniques to effectively reduce the number of transmitted parts. This paper presents quadtree partition technique as a pre-processing step in image processing to determine what part should be more heterogeneous than the others. It also introduces an idea to solve the problem of squared images. Finally, proposed approach is implemented and analysed. The simulation of the Matlab code of the quadtree is represented by all algorithms and the figures. Thus, achieved results are promising in the state of the art.     

Avoiding spots detection in analysis of electrophoretic gel images

Analysis of proteomic data, presented in form of 2D gel electropherograms, is still a real challenge and requires further improvements. Three main problems in data analysis are associated with (1) ‘software-induced variance’ (due to the different methods of data pre-processing or their different input parameters), (2) univariate significance analysis, and (3) requirements of spots detection and quantification. In our study, the advantages of multivariate significance analysis and a pixel-based approach are demonstrated upon the example of the real data set.     

基于FPGA设计的图像增强预处理器

就如何提高复杂背景条件下低信噪比的小目标检测概率问题展开讨论,提出了用数字滤波方法改善图像质量,提高信噪比,达到抑制背景噪声,增强小目标的目的。针对高帧频电视系统的特点,应用现场可编程门阵列（FPGA）构造高速图像预处理器,完成数字电视的图像预处理的实时计算,进而为后续工作打下良好的基础。仿真和实验结果证明是可行的。     

改善劣质实时图像预处理效果的优选法研究

针对某次实际飞行实验条件下所采集的有较严重问题的CCD实时景像图预处理算法研究问题，提出一种优选法。该方法先利用已有的Photoshop图形处理软件与人工手动调节相结合对原始图像进行处理，得到期望图像，再根据原图像的具体问题和已有的先验知识选择几种可能的预处理方法对原图像进行仿真，将所得图像与期望图像进行对比选出最适合处理此种劣质图像数据的优化预处理算法。将该算法应用于本次飞行实验数据上，飞行航线的匹配概率由5．41％提高到了86．48％，结果表明本文提出的优选法具有可行性和实用性。     

Optimal reduction of noise in image processing using collaborative inpainting filtering with Pillar K-Mean clustering

Digital image processing is a mechanism for analysing and modifying the image in order to improve the quality and also to manage the unwanted involvement of noises. In image processing, noise is characterized as an unwanted disturbance which occurs while capturing the actual image thus affecting the quality of the image. Hence, noise formation is considered as a perilous issue and the reduction of noise is considered as an awkward process. Nowadays, almost in all fields of science and technology, digital image processing is increasing rapidly, so there arises the need for de-noising to cure the noised image. The main objective of this paper is to overcome the issue of noise and also to increase the quality and pixel value of the image. An advanced methodology known as collaborative filtering and Pillar K-Mean clustering is discussed in this paper to overcome the abovementioned problem. Initially, distinct pure images are taken as the dataset and three types of noises are added to the corresponding image to make it as a noised one. Hence, the unspecified noise is resolved on the basis of a hybrid combination of algorithms of collaborative filtering with the image inpainting method. Sequentially, the low-density noises, such as random noise and poison noise, are recovered by the implementation of collaborative filtering, and the high-density salt and pepper noise are recovered by the image inpainting method. Based on the GLCM (Grey Level Co-occurrence Matrix) feature, the normal image and the noised image are used for the clustering process. Then the de-noised image is evaluated to find the efficiency on the basis of few parameters such as SNR (Signal to Noise Ratio), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and SSI (Structural Similarity Index). Accordingly, the evaluated images are further withstood for clustering to differentiate the noises by applying the proposed clustering methodology. Then the evaluated images are verified on the basis of a few parameters such as Silhouette Width, Davies–Bouldin Index and Dunn Index. The proposed methodology is run on the platform of Mat Lab. Finally, the proposed methodology is considered as an efficient method for settling the issue in digital image de-noising.     

全变分自适应图像去噪模型

通过分析三种主要变分去噪模型(调和、全变分以及广义全变分模型)的优缺点,提出了一种基于全变分的自适应图像去噪模型。该模型根据噪声图像的信噪比,采用高斯滤波器对图像进行预处理,克服了全变分模型引入的阶梯效应;利用图像中每一像素点的梯度信息,自适应选取去噪模型中决定扩散强弱的参数p(x,y),使接近边缘处平滑较弱,远离边缘处平滑较强。数值实验表明,本方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪性能,其峰值信噪比(PSNR)在高噪声水平下,较其他变分方法至少提高1.0dB左右。