danfu

一种可适用于复杂光照环境的低照度图像增强算法，即基于内容感知的自适应对比度提高的增强算法。引入了改进的伽马矫正，可以根据低照度图像的明暗程度自适应的对图像进行预处理。在实验过程中还发现，虽然改进算法有效避免引入大量噪声，但仍有少量噪声存在，为了达到更好的视觉效果，利用改进的双边滤波对增强的图像进行处理，获得最后的增强图像。实验效果显示，该算法可对低照度图像实现理想的增强效果。(A can be applied to complex light environment of low illumination image enhancement algorithm, which based on the content of perception adaptive contrast enhancement algorithm. Introduced the improvement of gamma correction, can according to the low illumination image of light and shade degree adaptive to image preprocessing. In the process of experiment also found that although the improved algorithm effectively avoid introducing a lot of noise, but there still have a small amount of noise, in order to achieve better visual effect, use of improved bilateral filtering to enhance the image processing, finally enhance image. The experimental results showed that the algorithm can achieve a low illumination image enhancement effect. )

计算二维或多维循环自相关函数和自相关密度函数(Of two-dimensional or multidimensional cyclic autocorrelation function and autocorrelation function of the density)

针对空间发光点缩成光斑图像中心的高精度提取问题，本文提出一种亚像素提取方法。该方法简历了具有高斯能量分布的空间发光点的透视投影光斑图像灰度分部数学模型，证明了空间发光点能量中心的透视投影不变性。(Light-emitting points of space shrunk to spot high-precision center of the image extraction problem, we propose a subpixel extraction method. This method resumes a space with Gaussian energy distribution points of perspective projection spot light gray segment mathematical model to prove the energy center of the space light-emitting point perspective projection invariance.)

编译好的tesseract 4.0库 for VS2015 (含lib、dll和头文件)，另附一个tessreact 4.0进行图片识别的示例程序，并给出运行具体配置方法！

全参考型视频质量评价方法，基于机器学习和视频特征提取，发表与2012年TMM(FR VQA method based on machine learning and video feature )

mean shift图像分割软件,只能对灰度图像进行处理,如果对彩色图像进行处理,需要转换(mean shift image segmentation software, only for grayscale image processing, if the right color image processing, needs to be converted)

介绍了一种典型的ＣＦＡＲ检测算法，使用ｍａｔｌａｂ进行仿真(CFAR detecting algorithm)

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).)

图像背景提取，Matlab写的，效果还不错。(Image background extraction)

IDL中用于读取ENVI格式的遥感卫星影像数据的IDL程序(IDL, ENVI format for reading remote sensing image data on the IDL)