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运用contourlet变换和小波变换对图像进行去噪处理,并将二者的去噪效果通过PSNR进行对比.文中包含去噪.m文件和求PSNR的.m文件.程序中注释详细,值得参考.通过测试的代码.-Use Contourlet transform and wavelet transform for image denoising treatment, and the denoising effect of the adoption of both PSNR comparison. Paper contains denoising. M documents and PSNR for the. M file. Program notes in detail, it is also useful. Through test code.

一个基于DCT变换的信息隐藏程序，能把文本信息隐藏到经过DCT变换的图像载体的频域中去。程序重点在于对图像的DCT变换-DCT-based Information Hiding Transform procedures, able to hide text message into image after DCT transform domain to the frequency vector. Procedures focus on the DCT transform of image

实例5 页码：34 稿件名称：VC图像处理－－快速傅立叶变换篇 稿件作者：耿迅 程序名称：ImageTest 运行环境：Win+VC6.0 注意事项： 由于本程序使用了VFW组件，所以在Project－－Setting中－－Object/libray modules中添加vfw32.lib。 -err

水平集图像分割方法的一个经典DEMO，05年CVPR上的文章的实现程序，无偿呈现-Level set image segmentation methods of a classical DEMO, 05 years CVPR articles realization procedures, free show

Visual C++下的小波变换，小波分解与重构，加入噪声分析，显示分解和重构图形。-Wavelets for data encode and uncode, the figures would be shown.

This toolbox implements the algorithm in a fairly general way in a C file that can be called from Matlab. It allows to perform the traditional NL-means for denoising (for both B&W and color images) but also to use an arbitrary set of patches to perform the denoising. -This toolbox implements the algorithm in a fairly general way in a C file that can be called f rom Matlab. It allows to perform the traditiona l NL-means for denoising (for both B

黑白显示，中值滤波，8，4邻域均值滤波，高通滤波，拉普拉斯锐化，手动阈值分割，二值化-Black-and-white display, median filtering, 8,4-neighbor mean filter, high pass filter, Laplacian sharpening, manual threshold segmentation, binarization

通过MATLAB中的rbg处理图像，利用此程序 确认图像中不完整的个体，并表示出来。-Through MATLAB treatment of RBG images, use this procedure to confirm the image of the individual is incomplete, and express them.

This Matlab code implements an edge based geometric active contour model without reinitialization. MATLAB Release: R13SP1 This Matlab code implements the level set method in Chunming Li et al s paper "Level Set Evolution without Re-initialization: A New Variational Formulation", in IEEE Proc. of CVPR 2005. EVOLUTION.m: core function that performs level set evolution. Demo1.m: a demo for a real cell images Demo2.m: a demo for a synthetic noisy image Demo1_ManualBinaryInitial: demo for the same image in Demo1, allows manual initialization by the user Demo2_ManualBinaryInitial: demo for the same image in Demo2, allows manual initialization by the user