image-convert

主分量分析的源代码，附有简单的解释说明，主要对图像矩阵做降维处理，适用多幅图像的变化检测(Principal component analysis of the source code, a simple explanation, the main image matrix reducing dimension, suitable for image change detection)

数字图像处理和图像识别的vc++编程方法(Digital image processing and image recognition of the vc++ Programming)

基于FRFT的数字图像水印（lena示例）(FRFT based digital image watermark (lena example))

利用双光子纠缠方法进行双路轨道角动量光学鬼成像过程。(Using two-photon entanglement the dual orbital angular momentum of optical ghost imaging process.)

说明：  基于MATLAB GUI的图像处理界面，包括基本的图像处理功能(Graphical interface construction based on image processing)

模拟运动模糊 恢复模糊图像 对加性噪声图像的恢复等(Fuzzy simulation of the restoration of motion-blurred images of additive noise, such as image restoration)

图象处理中经常用到图象抽取和插值，本程序就是常用的双线性插值法的实现(image processing frequently used images collected and interpolation, the procedure is commonly used by the bilinear interpolation to achieve)

完成对视频帧的四叉树分割，在纹理复杂处分割块较小，在纹理平坦处分割较大(Complete quadtree segmentation of video frames, complex area in texture segmentation smaller, flat larger regional segmentation block in texture)

完整的图像加密算法以及对此算法加密结果的性能分析：像素之间相关性，图像的信息熵，以及NPCR,UACI(completely figure encryption scheme and corespond result analysis)

提出了一种结合USFFT Curvelet 变换的各向异性扩散图像去噪模型。它有机结合了Curvelet 变换和各向 异性扩散（P-M扩散）两者的优点。通过P-范数方法选择合适的梯度阈值K，P-M扩散过程通过处理经过Curvelet 变换得到的图像的不同尺度的Curvelet 系数矩阵，实现了建立在对图像多尺度分析的基础上的新P-M扩散模型。 实验表明，新模型的处理结果能有效避免传统P-M 扩散出现的阶梯效应，同时更好地保留图像的纹理和细节。 关键词：图像去噪算法；各向异性扩散；Curvelet 变换；P-M 扩散(Abstract: An image de-noising model by integrating anisotropic diffusion with USFFT Curvelet transform was proposed, which combined the strongpoint of Curvelet transform with anisotropic diffusion (P-M diffusion). By choosing appropriate gradient threshold K through p-norms and carrying out the P-M diffusion process depend on the different scale matrixes of Curvelet coefficient of the image from Curvelet transform iterations, as a result, the improved model made it possible to carry out the new P-M diffusion de-noising process based on multi-scale analysis of the image. The experiment results have demonstrated that the model can avert the stair-casing effect in the traditional P-M diffusion effectively and keep the textures and details of images better.)