ExThreshold

图像预处理，包括灰度化、图像去噪、灰度变换、阈值分割、边缘检测，形态学处理(image preprocessing)

说明：  数学地角度来看，信号与图像处理可以统一看作是信号处理（图像可以看作是二维信号），在小波分析地许多分析的许多应用中，都可以归结为信号处理问题。对于其性质随时间是稳定不变的信号，处理的理想工具仍然是傅立叶分析。但是在实际应用中的绝大多数信号是非稳定的，而特别适用于非稳定信号的工具就是小波分析。(Mathematically, signal and image processing can be regarded as signal processing (image can be regarded as two-dimensional signal). In many applications of wavelet analysis, they can be reduced to signal processing. For signals whose properties are stable over time, the ideal tool for processing is still Fourier analysis. However, most of the signals in practical applications are unstable, and the tool especially suitable for unstable signals is wavelet analysis.)

用图像分割的方法提取树叶病斑坏死区域并显示(Necrotic lesion leaves the region extraction method, and displays the image segmentation)

数字图像处理有关于图像去雾的新型算法与实现(There are new digital image processing algorithms on the image to go to the fog and Implementation)

说明：   [Wang 1998]Spiral CT Image Deblurring for Cochlear Implantation

最近编写了一些极对数坐标转换的程序，这是最初编写的那个，希望对你们有用(Here is a log-polar image transform code.)

用块匹配法求超分辨率复原中的运动矩阵!提出了一种快速，半像素的三步法！(Block-matching method using super-resolution recovery of movement in the matrix! Presents a fast, three-step half-pixel!)

主成分分析程序,应用于图像特征提取，数据降维等方面 (the code of PCA)

：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)

数字图像处理，对彩色图像的拉普拉斯算子锐化以及梯度算子锐化的比较，VC/MFC实现。(Digital image processing, color image of the Laplacian and the gradient operator sharpening sharpening comparison, VC/MFC implementation.)