Imageembed

提出一种新的基于分支结构特征的视网膜图像配准方法，此算法可降低误匹配的点。能够应用到其他图像配准中，形成良好的等级结构。(Feature-Based Retinal ImageRegistration Using Bifurcation Structures)

这篇论文提出了一种较好的水下图像增强的方法。首先使用经过端到端训练的卷积神经网络去测量输入图片，同时以自适应双边滤波器对传输图片进行处理。接着提出一种基于白平衡的策略来消除图片的颜色偏差，用拉普拉斯金字塔融合获得无雾和色彩校正图像的融合结果。 最后，输出图像被转换为混合小波和方向滤波器组（HWD）域，用于去噪和边缘增强。 实验结果表明，该方法可以消除颜色失真，提高水下图像的清晰度。(This paper proposes a better underwater image enhancement method. Firstly, an end-to-end training convolutional neural network is used to measure the input image, and an adaptive bilateral filter is used to process the transmitted image. Then a strategy based on white balance is proposed to eliminate the color deviation of images. The fusion results of fog-free and color correction images are obtained by Laplacian pyramid fusion. Finally, the output image is converted into hybrid wavelet and directional filter bank (HWD) domain for denoising and edge enhancement. The experimental results show that the method can eliminate color distortion and improve the clarity of underwater images.)

说明：  模拟退火算法在图像处理中的典型应用，用matlab编程(Simulated annealing algorithm in a typical image processing applications, programming with matlab)

说明：  均值方差与协方差 协方差矩阵、特征值与特征向量的提取、PCA原理 人脸识别算法之EigenFace、LBPH 人脸识别应用开发案例(Mean Variance and Covariance Covariance Matrix, Extraction of Eigenvalues and Eigenvectors, PCA Principle EigenFace and LBPH of Face Recognition Algorithms Case Study of Face Recognition Application Development)

dct和混沌相结合的图像加密，速度较慢但可以用来学习(DCT and chaotic combination of image encryption, slower but can be used to study)

twain_vb源码,图像设备与计算机软件接口程序(twain_vb)

说明：  利用c语言快速实现各类小波变换，案例丰富(Using C language to realize all kinds of wavelet transform quickly, with rich cases)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)

说明：  本算法包括区域生长法和形态学后处理算法，功能实现了皮肤镜图像的病变区域分割，准确度很高，后处理算法对原图的影响很小；(This algorithm includes region growing method and morphological post-processing algorithm. It can segment the lesion area of dermoscopic image with high accuracy, and the post-processing algorithm has little influence on the original image.)

说明：  用数字图像处理方法提取书法特征 实现书法骨架提取(Using digital image processing method to extract calligraphy features)