WT1

说明：  Matlab曲波变换工具箱，安装在matlab上，可以应用曲波变换(Matlab curvelet transformation toolbox)

利用粒子滤波方法识别和跟踪动态目标，英文文献，经典(use the method of particle filter to track moving objects)

A Image converting Utility to scale the images

基于非下采样轮廓波变换的图像融合。低频分量取平均，高频分量取绝对值最大。(nonsubsampled contourlet transform.Averaged low frequency components, high frequency components of the maximum absolute value.)

基于偏微分方程的遥感图像处理方法 利用测试图像对算法进行仿真验证，并与其它图像去噪算法进行去噪效果比较，通过数值对降噪效果进行评估，得出该方法不仅能够去除噪声而且能够很好的保护图像的边缘等细节信息，弥补了高阶非线性扩散去噪方法导致边界模糊的缺陷，提高了遥感图像的质量，改善了视觉效果(Simulation algorithm of remote sensing image processing methods based on partial differential equations using the test image denoising results compare with other image denoising algorithm to assess the value of the noise reduction effect, draw the method can not only remove the noise but well protected image edge details, to make up for the higher order nonlinear diffusion denoising method lead to fuzzy boundaries defects and improve the quality of remote sensing images and improve the visual effects)

shi-tomasi方法角点提取方法。该方法测量结构张量的最小特征值。这代表了Harris和诺贝尔探测器试图近似的理想。哈里斯希望在计算特征时避免计算平方根的计算成本。(Shi-Tomasi corner detector method.The method measure returns the minimum eigenvalue of the structure tensor. This represents the ideal that the Harris and Noble detectors attempt to approximate. Back in 1988 Harris wanted to avoid the computational cost of taking a square root when computing features. This is no longer relevant today!)

论文+ppt，我的数字图像处理课程设计，题目是基于均值滤波和小波分析的图像去噪方法(Thesis+ppt, digital image processing course design, subject image denoising method based on mean filter and wavelet analysis)

读取gpr数据，并且对数据进行成像，没有进行任何数据处理(Read the GPR data and image the data without any data processing)

labview图像预处理，子程序,包括转化为灰度图，二值化，腐蚀，膨胀，参数可调(labview IMAQ image pretreatment,sub vi，consist of conversion to grayscale, binary, erosion, dilation, adjustable parameters)

自然图像的梯度分布为重尾分布，超拉普拉斯分布，本代码通过概率统计画出梯度分布的曲线(The heavy-tailed distribution of gradients in natural scenes have proven effective priors for a range of problems such as denoising, deblurring and super-resolution.These distributions are well modeled by a hyper-Laplacian)