super_resolusion-pocs

说明：  1. 对图像进行傅里叶变换。 2. 根据傅里叶变换的结果去除图像的竖条纹噪声。 3. 对图像进行傅里叶逆变换，得到分割图像。 (1. For the image Fourier transform. 2. According to the results of Fourier transform to remove the image of the vertical stripe noise. 3. Inverse Fourier Transform of the images obtained segmented image.)

说明：  基础图像处理语言，RGB各图层提取，量化图像，lena典型案例代码。(Basic image processing language, RGB layer extraction, quantization image, Lena typical case code.)

基于图像灰度共生矩阵的特征提取代码，程序实现0 45 90 135度4个方向的特征提取(GLCM based on image feature extraction code, the program achieved 04,590,135 degrees in four directions of the feature extraction)

对图像进行GMM（混合高斯）拟合后用EM算法进行分割(Image GMM (Gaussian Mixture) after fitting algorithm using EM Segmentation)

李纯明博士的无需初始化模型的源代码，作者亲自写的。还有测试图象，相信对大家肯定有好处。(Economics Dr. initialization model without the source code, written by the author himself. There test image, I think we will benefit.)

说明：  用labview实现图像的二值化，通过把图片转化为二维数组，通过和阈值比较把图像二值化(Achieved with labview binary image by the image into a two-dimensional array, and the threshold value by comparison to image binarization)

实现bmp位图的图像放大缩小，包含整个mfc的源码文件。(Achieve bmp bitmap image zoom, contains the entire mfc source files.)

图像去噪,应用DTCWT 进行地震信号的去噪和重建(picture-denosing apply DCTWT into signal of earthquake denosing)

代码实现2014 CAD 文章 Progressive and iterative approximation for least squares B-spline curve and surface fitting(The code implementation of Progressive and iterative approximation for least squares B-spline curve and surface fitting)

说明：  ：针对显微图像分析、识别需要的全自动控制显微镜，研究开发其相应的图像处理算法。提 出了能量谱自动聚焦评价函数算法、自适应选取基准图的图像拼接算法、改进 Laplacian算子的多层 聚焦图像叠合算法等。该算法模块已成功地应用于所开发的CMIAS 显微医学图像分析系统中，取得 了满意的应用效果。(【ABSTRACT】For the automatic microscope required by micro-image analysis and recognition, the relevant image processing a lgorithms are investigated. Three algorithms are described: energy-spectrum image auto focusing evaluation function, image merging based on the adaptive selection of standard image and sum-modified-Laplacian (SML) operator for multi-focus image fusion. The algorithms have been applied successfully to CMIAS medical micro-optical image analysis system and testified to be feasible and effective. )