CT影像噪声的检测与评价

ＣＴ影像噪声的检测与评价黄大同ＣＴ影像噪声，是指ＣＴ设备对某一均匀物体（如水模）成像时，影像区域内的ＣＴ值相对于其平均值的标准差，通常以δ表示。噪声水平是与成像质量密切相关的性能参数，它直接影响和限制着系统的低对比度空间分辨率。因此，噪声测量是ＣＴ设...

采用变分法的遥感影像条带噪声去除

为了避免去除遥感影像条带噪声时丢失影像细节,构建了综合两种单向变分模型的组合去噪方法。首先,构建一种条带去除单向变分模型,通过此模型可以有效去除遥感影像条带噪声,从而初步获得近似遥感影像及条带噪声影像,同时避免近似遥感影像出现阶梯效应;其次,为了避免条带噪声影像中包含的少量细节信息丢失,构建一种条带保留单向变分模型,通过此模型可以去除条带噪声影像中的细节信息而只保留条带噪声,从而有效分离出细节信息;最后,把分离出的细节信息与近似遥感影像进行累加得到去噪影像。通过实验分析发现,本文方法不仅能完全去除条带噪声,而且基本没有破坏影像细节,去噪后的影像质量得到明显提高。

基于离散平稳小波的影像恢复去噪方法

为了避免图像去噪过程中图像边缘细节信息大量丢失、去噪图像出现模糊及阶梯效应等问题,本文提出一种基于离散平稳小波(SWT)的图像恢复去噪算法。首先,对噪声遥感图像进行3层SWT分解;其次,构建图像恢复去噪算法,并采用此算法对每一层的高频分量进行去噪处理;最后,对低频分量和去噪后的高频分量进行SWT重构,获得去噪图像。实验结果表明,本文提出的图像去噪算法不仅可以有效地抑制图像的随机噪声,而且能在去除图像随机噪声的同时保留更多图像的边缘细节信息,去噪图像呈现较好的视觉效果。

一种新的组合滤波算法及在影像去噪中应用

为进一步研究遥感影像的噪声处理技术,针对传统2层滤波方法中的阈值判别量不确定和噪声残余问题,提出了一种融合自适应Wiener滤波、BayesShrink阈值滤波和中值滤波技术的3层组合滤波算法.该算法在传统2层滤波基础上增加自适应Wiener滤波,并引入拉普拉斯算子提取影像边缘细节,最后再进行图像融合.通过仿真测试和工程试验,证明所提算法能够有效去除混合噪声残余项,使得影像更加清晰,效果明显优于传统2层滤波技术.

Landsat影像垂直方向条带噪声去除方法研究

针对条带噪声去除过程中容易丢失细节信息、去噪后的影像出现模糊及阶梯效应等问题,提出两种变分模型去除条带噪声.首先,构建一种初步条带噪声分离模型(PSDM),通过交替方向乘子方法同时解求出恢复影像及条带噪声影像;其次,构建一种条带噪声变分模型(SNVM),此模型可以达到去除条带噪声影像中的细节信息而只保留条带噪声的目的;最后,把恢复影像与条带噪声影像中的细节信息进行累加获得去噪影像.实验分析结果表明:该方法能保证在影像扭曲度很小的情况下有效去除条带噪声,并且在影像细节保留及去噪后影像质量提升两方面都是最优的.

地理国情普查中地表覆盖自动解译问题研究

针对地表覆盖自动解译时经常遇到的分割尺度设置、影像噪声、判读差异、DLG数据与影像不一致等问题,在实际应用中,根据影像分辨率与测区地类设定合适的分割尺度,采用合理的解译顺序、调整分割尺度和采样率来降低噪声影响,用人机交互解译来解决判读差异问题,人工解译与现场核实相结合判断DLG数据与影像不一致区域。以上措施较好地解决了自动解译遇到的问题。

Tensor based non-photo realistic rendering for Korean architectural images

Existing pen and ink sketch technologies can be applied to general images, but they could not produce optimal output for images of traditional architecture, because most images consist of exquisite straight lined patterns in traditional architecture, such as root tiles and window bars. The lines of roofs and eaves need to be described delicately to express pen and ink sketch most effectively. Therefore, by proposing a method to create white noise for light and shade of input images, to extract input vector directions from the white noise, and to determine the direction and length of stroke, a new expression technique is proposed for pen and ink sketch that could best reflect the characteristics of traditional architecture.

Noise Detection in the Photogrammetric Images Using Wavelets

The paper presents the method of the valuation of random noise in the photogrammetric images, based on wavelets. The proposed method involves the analysis of the dynamics of the components of wavelet decomposition on several resolution levels. The hypothesis was made that the noise-free images are characterized by systematically growing variances of the single components with growing decomposition. This hypothesis was. studied on several dozen fragments of airborne images recorded both with a photogrammetric analogue camera and digital camera. For all the studied photos taken with a digital camera, the hypothesis of growing variances of details was confirmed. The images from an analogue camera had different dynamics of variance, and the cause was recognized as random noise, caused by the grains from of the photographs. Referring to earlier applications of wavelets to noise evaluation, the proposed method is characterized by smaller dependence upon the structure and texture of the image.

“句芒号”激光光斑质心提取方法与稳定性分析

根据句芒号足印影像特点,提出一种针对复杂背景噪声的激光光斑质心提取方法:基于灰度转换模型去除地物背景,结合距离约束、高斯滤波和大津阈值分割去除影像噪声,并利用灰度重心法计算质心坐标。使用所提方法对多种地物类型仿真光斑和实测光斑进行质心提取,结果表明:所提方法的质心提取均方根误差约为0.074 pixel,最大误差约为0.482 pixel,对句芒号光斑影像的适应性较好。对句芒号足印影像光斑开展了稳定性分析,结果表明,所有激光波束的光斑质心坐标的标准差小于0.34 pixel,激光器与光轴监视相机的相对几何关系较为稳定。所提方法可用于激光光斑质心提取及稳定性监测,对句芒号激光数据处理和应用有一定参考价值。

Technique Based on Image Pyramid and Bayes Rule for Noise Reduction in Unsupervised Change Detection

In this paper,a technique based on image pyramid and Bayes rule for reducing noise effects in unsupervised change detection is proposed.By using Gaussian pyramid to process two multitemporal images respectively,two image pyramids are constructed.The difference pyramid images are obtained by point-by-point subtraction between the same level images of the two image pyramids.By resizing all difference pyramid images to the size of the original multitemporal image and then making product operator among them,a map being similar to the difference image is obtained.The difference image is generated by point-by-point subtraction between the two multitemporal images directly.At last,the Bayes rule is used to distinguish the changed pixels.Both synthetic and real data sets are used to evaluate the performance of the proposed technique.Experimental results show that the map from the proposed technique is more robust to noise than the difference image.