MSE

基于深度学习的图像超分辨率算法。 参考论文 Learning a Deep Convolutional Network for Image Super-Resolution (ECCV 2014) (Super resolution based on deep learning Refer to Learning a Deep Convolutional Network for Image Super-Resolution (ECCV 2014))

该文件夹包括基于邻阈的小波图像去噪程序。主程序为wave_neighbor.m，实现3*3和5*5邻域去噪；wov_win.m为函数m文件，实现3*3和5*5的滑动窗口；cacupsnr.m为函数m文件，计算mse和psnr等。(This is about image denoising with the method of wavelet.It includes 3×3 and 5×5 neighborhood window.)

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. )

jpeg压缩的matlab程序，包括分块，量化，霍夫曼编码，其中量化和霍夫曼编码用查表实现，提高速度。(jpeg compression matlab procedures, including block, quantization, Huffman coding, in which quantization and Huffman coding using look-up table to achieve, improve the speed.)

基于Hog特征 SVM分类器，利用Opencv3.0进行手写数字识别的源代码及所需资源文件（训练图片、测试图片）

图像处理算法,实现多种对于灰度/二值图像的基本算法,包括灰度直方图,均衡化,门限处理,模糊,锐化,腐蚀,膨胀,掩模过滤,频域转换,边缘检测,梯度检测,二值化等等.实现的方法较多,且均经过一定的优化,效率不错.(Image processing algorithms to achieve a variety of for the gray/binary image of the basic algorithm, including the gray histogram, equalization, threshold treatment, blur, sharpen, corrosion, expansion, mask filter, frequency domain conversion, edge detection, gradient detection, binarization and so forth. The way to achieve more, and have to undergo a certain degree of optimization, efficiency, good.)

进行图像区域合并操作,能够较为有效，较好的进行图像合并(combine the image，very effcient)

立体匹配中的单目标定,输入图片，得出结果，官方实例(Monocular calibration)

说明：  高斯混合模型实现图像分割分类，包含中文注释。(Gaussian mixture model implements image segmentation and classification, including Chinese annotation.)

本文献讲述了使用多维的结构张量进行基于分水岭的图像增强和去噪方法(The literature describes the use of multi-dimensional structure of tensor-based watershed image enhancement and denoising)