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Retinex理论的基础理论是物体的颜色是由物体对长波（红色）、中波（绿色）、短波（蓝色）光线的反射能力来决定的，而不是由反射光强度的绝对值来决定的，物体的色彩不受光照非均匀性的影响，具有一致性，即retinex是以色感一致性（颜色恒常性）为基础的。不同于传统的线性、非线性的只能增强图像某一类特征的方法，Retinex可以在动态范围压缩、边缘增强和颜色恒常三个方面打到平衡，因此可以对各种不同类型的图像进行自适应的增强。(The basic theory of Retinex theory is the color of the object by object for the long wavelength (red), medium (green), short wave (blue) light reflection ability to decide, rather than by the intensity of reflected light of the absolute value to decide, the color of the object under illumination nonuniformity effects, consistent with the Retinex, that is based on color constancy (color constancy based). Different from the traditional linear, nonlinear image enhancement method of only one kind of features, Retinex can be in dynamic range compression, edge enhancement and three aspects of color constancy hit the balance, thus enhancing adaptive to a variety of different types of images can be.)

M估计子，计算机视觉大牛Peter Meer出品。(Generalized Projection based M-estimator C++ code to find the robust estimate derived without using any user supplied scale. The theory is described in Generalized Projection Based M-stimator. )

在VS2012平台上利用C++语言和opencv实现对图像的分块，并保存分块图像(Using the C++ language and OpenCV to block the image on the VS2012 platform and save the block image)

完成流程编码和huffman编码，适合做图像编码的熵编码，只要调整接口就好。(Complete the process encoding and huffman coding, image coding suitable for entropy coding, as long as the adjustment of the interface is like.)

：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提 出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)

对各种格式图像实现打开保存旋转的操作的示例工程的说明文档。(images of various formats to achieve preservation of open rotation examples of the operation of the project documentation.)

有关彩色图像分割与复杂场景下目标跟踪方法研究的一篇博士论文(On the color image segmentation and complexity of the scene target tracking methods under study for a Ph D thesis)

图像处理系列之边缘检测篇 涵盖内容： 1>Roberts算子 2>Prewitt算子 3>Sobel算子 4>Laplacian算子(Edge Detection)

我写的贝叶斯fusion的Visual C+++源码，简明易懂，提供参数说明 可直接使用。 (The Bayesian fusion the Visual C+++ of what I wrote source, easy-to-understand parameters can be used directly.)

说明：  用MFC实现将位图进行直方图均衡化处理的源码(MFC to achieve the bitmap with the histogram equalization processing of the source)