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Digital Image Filtering

本程序基于Matlab,初步实现图像增强的基本操作,实现 图像明暗度调整， 图像旋转，更改颜色显示方式，直接灰度调整，图像求反，动态范围压缩，显示原始图像直方图，直方图均衡化，滤波增强等功能-This procedure based on Matlab, the initial realization of the basic operation of image enhancement, image brightness adjustment, image rotation, change the color display, direct gray-scale adjustment, for anti-image, dynamic range compression, display the original image histogram, histogram equalization , enhanced features such as filtering

SIFT算法由D.G.Lowe 1999年提出，2004年完善总结。后来Y.Ke将其描述子部分用PCA代替直方图的方式，对其进行改进。 2 SIFT 主要思想 SIFT算法是一种提取局部特征的算法，在尺度空间寻找极值点，提取位置，尺度，旋转不变量。 -a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images.

图像的几何变换的相关算法：垂直与水平镜像，图像的缩放与旋转等-Geometric transform correlation algorithm: vertical and horizontal image, image scaling and rotation, etc.

来自于VC++数字图像处理典型算法及其实现这本书，对于初学者有很大的帮助。-very usrful,from a classical book

本人用matlab自己做的图像分裂合并程序，主要对印章图像进行处理．-own image split the merger process, mainly to seal image processing.

将彩色图像转换成灰度图像的程序，也可以转换成二值图像。-Color images will be converted into gray-scale image of the procedure can also be converted into binary image.

DCT变换的源代码，支持一维二维DCT变换及反变换-DCT transform of the source code, to support the one-dimensional two-dimensional DCT transform and inverse transform

（周长发）精通VisualC++图像处理编程的源代码，希望对大家有用-(Week long) proficient VisualC++ Image processing programming source code, in the hope that useful to everybody

利用阴影恢复技术重建二维数字图像到三维深度数据的重建-Use of the shadow of the resumption of technical reconstruction of two-dimensional digital image to the three-dimensional reconstruction of the depth of data