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4_1：生成模糊化实验图像 4_2：维纳滤波复原 4_3：约束最小二乘滤波复原 4_4：Lucy-Richardson滤波复原 4_5：盲卷积滤波复原(4_1: generate fuzzy images of experimental 4_2: 4_3 restoration Wiener filtering: constrained least squares filtering recovery 4_4: Lucy-Richardson filter recovery 4_5: Blind deconvolution filter to recover)

说明：  该算法主要包含PCA算法和K-Means聚类算法，用于SAR变化检测,包含数据图片。(The algorithm mainly includes PCA algorithm and K-means clustering algorithm for SAR change detection, including data images.)

修改过的pca-sift程序 可以正常运行(Modified pca-sift program runs)

基于双边滤波算法对原始图像进行增强，输入文件要求是ppm(image enhancement through recursive_bilateral_filtering)

关于图像处理程序，它能实现对破损图像的提取和修复，速度快( Image processing program, it can achieve the extraction and repair damaged images, fast)

实现了C聚类以后的马尔科夫图像分割，对阈值的选取和图像的后验概率计算(C clustering after the Markov segmentation, the posterior probability threshold value is selected and images calculated)

图像金字塔分解与图像金字塔重构matlab 程序，我试过了，很不错(Image pyramid decomposition and reconstruction of image pyramid matlab program, I tried, very good)

利用欧式度量聚类真实数据集和实际数据集用于对各类数据分类 (European metric clustering real data sets and real data sets for various types of data classification)

基于拓展RPC模型的多源卫星遥感影像几何处理 转化程序(RPC model-based development of multi-source remote sensing image processing conversion process geometry)

针对粒子概率假设密度(PH D)滤波算法在虚警、漏检情况下，b标状态估计不稳定和b标可观测性车弱的问题，提出了一种基于序贯融合的粒子PH D滤波方法，利用雷达和红外传感器多目标进行融合跟踪杏基本思想是先对红外传感器进行粒子PH D滤波，丙将红外传感器滤波结果作为雷达的预测值，然后利用P达观测的数据进行更新，这样通过雷达和红外传感器交替上作保证目标状态的可观测性，从滤波器输出结身即可得到目标的状态信息仿真结果表明，在虚警、漏检和密集目标环境下，该方法是有效的和稳健的(The problem of target state estimation instability and observability weakerin the presence offalse alarms and missed detection was deal with. On the basis of sequential fusion, a particleprobabil- ity hypothesis density(PHD) filter for multrsensor multrtarget tracking was proposed. Observed da to collected from the infrared sensor was estimated with the particle PHD filter. Then the results from the filter were set as the radar predicted value by the radar observations. The multrtarget state can be updated to guarantee observing the target state. In this way, the global state is updated at the fusion center. Simulation results show that the proposed algorithm is effective and robust under the false warning, omission and concentrated target environment. )