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图像变换检测 主成分分析法 含PDF介绍(iamge change detection using PCA)

用c语言实现的FIR算法 高通滤波器

针对多聚焦图像融合，本文首先介绍了小波变换融合算法，虽然小波变换方法无冗余，高频分量损失较小，具有较好的融合效果，但存在两个主要缺点：一是移变性，二是融合图像在重构时会受到一些外来因素的影响，所以仍会丢失一些边缘信息，为了克服小波变换法的以上缺点，可以采用基于小波分解和点扩展函数模型PSF相结合的融合方法。该方法首先对不同的图像进行无降2采样的小波分解，以保持与源图像大小相同，然后分别将其各方向、各尺度的高频分量进行叠加，并依此提取其高频分量的特征信息来判定清晰目标或模糊目标，以此来设计融合算法，采用一定的策略将两副源图像进行融合，从而避开了小波的反变换，因此融合后的图像具有比小波变换法更好的融合效果。(multi-focus image fusion, this paper introduces the wavelet transform algorithm, Although the wavelet transform non-redundant, the loss of high-frequency components are smaller, better integration effect, But there are two major drawbacks : First, the shift variability and the second is the integration of image reconstruction will be subject to a number of external factors. Therefore, there would still be some marginal loss of information, in order to overcome the wavelet transform of the above shortcomings, can be used based on wavelet decomposition and the point spread function PSF model combining the fusion method. The first pair of different images ratchet two sampling wavelet decomposition, and to maintain the same source image size. Then all of its direction, the scale of high-freque)

基于FFT的配准程序，用在图像配准中的粗匹配中，对噪声、图像中物体发生运动等极为鲁棒！ (FFT-based registration procedure, used in image registration in the coarse matching, noise, motion and other image objects occur very robust!)

基于Bandlet小波的图像去噪研究，具有较好的效果。(Bandlet wavelet-based image denoising, with good results.)

本程序实现灰度图像各向异性扩散(Anisotropic Diffusion)滤波算法，可以用于平滑信号，保留边缘，增强边缘对比度。与普通高斯Gaussian算法相比，它不会平滑边缘的图像。(The program implements grayscale image using anisotropic diffusion filtering algorithm.It can be used to smooth the signal, keep the edge, enhance the edge of the contrast. Compared with ordinary Gaussian algorithm, it does not smooth the image edges.)

一种改进的基于蚁群算法的图像边缘检测文献，前沿论文(ant colony system,matlab)

研究生机器视觉课程的大作业 运动目标检测 (moving target detect)

对图像进行高通滤波和低通滤波处理，包括图片和测试程序。(High-pass filter the image and low-pass filter, including pictures and test procedures.)

《信息隐藏实验教程》源代码，希望对大家有所帮助(" Information Hiding experimental course," the source code)