实现数字水印算法，抗剪切，抗噪声，但不抗旋转。

该源码是基于图像处理的计算机集成数控技术。-the source is based on image processing computer integrated CNC technology.

行程编码，JPEG压缩编码（基本系统） 命令行编译过程如下 vcvars32 rc bmp.rc cl compress.c bmp.res user32.lib gdi32.lib-itinerary coding, JPEG coding (basic system) command line compiler process following vcvars32 mail c bmp.rc l compress.c bmp.res user32.lib gdi32.lib

用MATLAB一次性快速过滤图像得到2G Gaussian Model. 查看spacefilter.m.-MATLAB rapid one-time images to be filtered Gaussian Model 2G. View spacefilter.m.

图形文字处理编程,查找并定位文字串,可以用于图象处理-GDI+ graphics handle language programming, search and locate the text string

通过串口控制CA210和Quantdata802BT，实现Gamma和色温曲线的自动测量-????

此程序用于中值滤波 采用了一定的算法 滤波效果比较好-medium

VC开发的视频监视器,支持摄像头,能保存图像,图片,异形窗体.-VC development of the video monitor, and support camera can save images, photos, special-shaped windows.

沃尔什（Walsh）变换是一种可分离变换，广泛应用于图像处理。本代码实现了图像Walsh变换，非常经典，带图片可测试.-Walsh (Walsh) is a separable transform transform, which are widely used in image processing. Implementation of the code of the image Walsh transform, very classic, with pictures can be tested.

含噪数字图像基于小波变换的去噪及效果评价-Noisy digital image denoising based on wavelet transform and Evaluation of

：由于许多传统的去噪方法在强背景噪声情况下提取声音信号的能力变弱甚至失效， 提出 应用独立成分分析( I C A) 方法对声音信号进行特征提取， 并证明了这种 I C A 变换能增强语音和音 乐信号的超高斯性． 在此基础上， 应用 I C A基函数作为滤波器， 通过阈值化的去噪方法对含有强高 斯背景噪声的声音信号进行去噪仿真实验． 结果表明， 本方法明显优于传统的均值滤波和小波去噪 方法， 为强背景噪声下弱信号的检测提供 了新的途径．-: As many of the traditional de-noising method in case of strong background noise, the ability to extract the voice signal even weaker failure, the application of independent component analysis (ICA) method of voice signal feature extraction, and prove that this transformation can be enhanced voice ICA and music of super-Gaussian signals. On this basis, the application of ICA basis function as a filter, through the threshold of the denoising method of Gaussian background noise contains strong voice signal denoising simulation. The results show that this method is obviously superior to the traditional mean filtering and wavelet denoising methods for the strong background noise under the weak signal detection provides a new way.