DWT-watermark

显示不同类型的色表，并查找相应区域或位置的值。 基于idl编程 (Show the different types of color tables, and find the corresponding value of the region or location. Idl-based programming)

视频演示算法包括： 1. 静态背景下的背景预测法目标检测 2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法 4. 重心多目标跟踪方法 (Algorithm for video presentation include: 1. Static background background prediction target detection 2. Static background frame difference method for target detection 3. Mean Shift tracking method 4. Gravity multi-target tracking method)

说明：  利用CFAR检测SAR图像中的高亮度目标。(Detect targets with high backscattering intensities by CFAR algorithm)

介绍近场声全息的参考资料，其中格林函数对全息效果的影响是比较好的参考，有助于对全息的理解(Introduction nah reference materials, which affect the Green' s function of holographic effect is relatively good reference, contribute to the understanding of holography)

本书是计算机视觉编程的权威实践指南,依赖 Python 语言讲解了基础理论与算法,并通过 大量示例细致分析了对象识别、基于内容的图像搜索、光学字符识别、光流法、跟踪、三维重建、 立体成像、增强现实、姿态估计、全景创建、图像分割、降噪、图像分组等技术。(This book is an authoritative guide to computer vision programming, which explains basic theories and algorithms based on the Python language.A large number of examples carefully analyze object recognition, content based image search, optical character recognition, optical flow, tracking, 3D reconstruction,Stereoscopic imaging, augmented reality, attitude estimation, panoramic creation, image segmentation, noise reduction, image grouping and other techniques.)

采用MFC，实现遥感图像的基本增强，滤波，变换等功能。还可以保存数据。(Using MFC, to achieve the basic enhancement of remote sensing images, filtering, transformation and other functions. You can also save data)

说明：  super resolution可实现 很多算法(a lot of super resolution algorithms can be realized)

说明：  残差灰色模型的改进算法。可以得到高精确度的预测，很实用，并附有测试文件(Improved algorithm for gray model residuals. Can be predicted with high accuracy, very practical, with a test file)

利用高斯混合模型进行视频前景运动目标的提取！请使用自己的AVI文件。(extract the foreground moving object using the Gaussian mixture model)

通过阈值去噪，最关键的就是要确定最佳阈值T 的大小.如果阈值太小，可以保留更多的小波系数，但同时噪声也被保留；相反，阈值太大，可以消除更多的噪声，但重要图像特征又将被滤掉，引起偏差.目前使用的阈值分为全局阈值和局部阈值.全局阈值对各层所有的小波系数或同一层内的小波系数都是统一的；局部阈值则是根据当前系数周围的局部情况来确定合适的阈值，具有灵活性.(Threshold denoising, the most critical is to determine the optimal threshold T s size. If the threshold is too small, you can retain more of the wavelet coefficients, but the noise was retained the contrary, the threshold is too large, can be eliminatedmore noise, but the important image features in turn be filtered out, causing bias current threshold is divided into a global threshold and local threshold. global Threshold the wavelet coefficients of all the wavelet coefficients of the layers or on the same floorare unity local threshold is based on the local situation of the current coefficient around to determine the appropriate threshold, with flexibility.)