Kriging

Harris角点检测算法,已经测试通过,程序中有清晰的注解，对于算法的理解有较大的帮助．(Harris corner detection algorithm has been tested, there is a clear process of annotation, the algorithm has a greater understanding help.)

目标分类（流形学习slle+支持向量机svm）(Target classification (manifold learning slle+ support vector machine svm))

基于堆栈的图像分割算法，速度较普通区域生长法更快(Stack-based image segmentation algorithm, region growing faster than the ordinary method faster)

lsb空域水印算法，dct水印算法，dwt水印算法，处理灰度水印以及彩色图像水印，同时还有字符水印嵌入音频中。分别有着三个GUI界面实现不同功能。欢迎批评指正！(lsb airspace watermarking algorithm, dct watermarking algorithm, dwt watermarking algorithm, the watermark process grayscale and color images watermarking, as well as the characters in the watermark embedded audio. Respectively GUI interface has three different functions. Welcome criticism correction!)

主要是关于模糊熵的，里面还和灰色关联度有点联系。搞图像的应该可以看的懂的。(Mainly on fuzzy entropy, which has little contact and the gray. Should be able to read out the image of understand.)

高光谱图像MNF算法，实现高光谱图像的去噪，注释详尽(Hyperspectral image MNF algorithm to achieve denoising hyperspectral images, detailed comments)

视网膜血管检测的Gabor变换和机器学习，教程 本教程将演示如何Gabor变换和广义 的线性模型（GLM）可用于视网膜血管检测 图像。 ，我们将尝试检测视网膜血管从 的训练图像，首先，Gabor滤波器与图像卷积。 GLM将使用Gabor变换的图像特征确定 （独立变量）和容器的位置 为结果（因变量）。( Retinal Vessel Detection by Gabor Transform and Machine Learning, a Tutorial This tutorial will demonstrate how Gabor transforms and generalized linear model (GLM) can be used for detection of retinal vessels in images. Specifically, we will attempt to detect the retinal vessels a training image , by first, convoluting multiple Gabor filters with the image. A GLM will be determined using the Gabor transformed images as features (the independent variables), and the locations of the vessels as the outcome (the dependent variable).)

对图像进行二维离散小波变换, 变换级数大于等于3级,然后进行阈值化处理(阈值约为10左右),再统计系数中0的个数（百分比表示）并进行重构, 最后计算重构图像的峰值信噪比(PSNR). (Two-dimensional discrete wavelet transform image transformed series is greater than or equal to 3, and then processing thresholding (threshold of about 10 or so), then statistical coefficient in the number (expressed as a percentage) and to reconstruct the final calculation reconstructed image of the peak signal-to-noise ratio (PSNR).)

使用python语言实现多张图像的拼接，并且处理拼接之后的裂缝和黑边。(Use python language to achieve splicing of multiple images, and deal with cracks and black edges after splicing.)

很有用的彩色图像分割方法，经本人实验，绝对可靠(Very useful for color image segmentation method, by my experiment, absolutely reliable)