lowfrefus

基于SIFT的一种新特征点匹配算法,比SIFT还强,(Based on SIFT feature points of a new matching algorithm is also stronger than the SIFT,)

是图像处理中常用的测试图库，非常实用,如lena图片， 青椒图片，以及zelda图片等(is the test pictures used in image processing very useful)

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)

彩色图像的威纳滤波去噪算法，很简单的，很全，自己毕业论文的程序(Color image of the Wiener filter denoising algorithm, very simple, very full, their thesis program)

基于DCT变换的彩色图像压缩；本程序的压缩率高，可达16+，图像恢复出来的效果较好(Color image compression based on DCT transform)

图像修复是对图像中破损区域进行信息填充，以减少图像破损所带来的信息损失的过程。 传统的图像修复方法需要依赖图像的具体结构来制定相应的修复方法，压缩感知理论的提出，使得可以利 用信号的稀疏性来对图像进行修复。基于K 奇异值分解（KSVD）与形态学成分分析（MCA，Morphological Component Analysis）的图像修复方法首先采用形态学成分分析方法对破损图像进行特征分析，将其分解 为结构部分和纹理部分；然后基于学习型字典KSVD分别对这两部分进行过完备字典训练；最后利用训练得 到的字典实现对破损图像的修复。相比于传统的图像修复方法，该方法具有适应性强、修复效果好等优点(Image inpainting is to fill the missing data in corrupted images and thus to reduce the information loss of damaged image. Traditional inpainting algorithms are dependent on specific structure of target images compressive sensing theory makes is possible to realized image inpainting with signal sparsity. This paper proposes a novel inpainting algorithm based on KSVD and MCA algorithm, which first decomposes the image into texture part and structure part, and then trains the two dictionaries for these two parts with KSVD and reconstructs the original image with these two trained dictionaries. Experiment indicates that the proposed algorithm is of better adaptability and performance as compared with traditional algorithms.)

基于蚁群算法的边缘检测,能够准确定位图像的细节和边缘，清晰的再现图像的轮廓(An Ant Colony Optimization Algorithm For Image Edge，could position the edge of image pricisely .)

truncated DCT. good for video compression.

可以实现图像的重采样，通过设置参数来减小或增大图像尺寸(Resampling of the image can be achieved by setting parameters to decrease or increase the image size)

用小波去噪，不同尺度下的阀值去噪，去噪效果的对比(With wavelet denoising threshold denoising different scales, denoising effect contrast)