DLTs-L-approximation-code

AForgeNet 是基于.net框架使用C#高级面向对象语言设计开发的专门为开发者和研究者学习和使用的开源图像处理类库，包括计算机视觉与人工智能，图像处理，神经网络，遗传算法，机器学习(AForgeNet is an open source image processing class library designed and developed for developers and researchers based on. net framework using C # advanced object-oriented language. It includes computer vision and artificial intelligence, image processing, neural network, genetic algorithm and machine learning.)

说明：  自己实现的BM3D图像去噪算法。它首先把图像分成一定大小的块，根据图像块之间的相似性，把具有相似结构的二维图像块组合在一起形成三维数组，然后用联合滤波的方法对这些三维数组进行处理，最后，通过逆变换，把处理后的结果返回到原图像中，从而得到去噪后的图像。(Image Denosing by Sparse 3-D Transform-Domain Collaborative Filtering.)

说明：  视频转帧预处理 ，应用于红外图像序列处理，为后续处理提供便宜。(Video frame preprocessing, applied to infrared image sequence processing, provides cheaper for subsequent processing.)

使用echat 绘制的地图 百度地图api(Map drawn using echat baidu map api)

ansys中建立的塔线模型，方便后续计算(Tower-line model established in ANSYS is convenient for subsequent calculation)

说明：  使用python实现图像的加解密算法，涉及到基于RC4的图像加解密，基于logistic的图像加解密，arnold图像置换，有图像界面，使用QT制作(Using Python to implement image encryption and decryption algorithm involves image encryption and decryption based on RC4, image encryption and decryption based on logistic, Arnold image replacement, image interface, QT production)

基于MATLAB的图像二维熵的计算，采用了改进的均值滤波器模板，使结果与物理意义更加契合。(This program calculates the 2-dim entropy of images, which is based on MATLAB. It uses a better average filter template, and makes the result in accordance with the identification of entropy.)

LVQ(学习矢量量化)算法：它是Kohonen的有监督学习的扩展形式,融合了自组织和有导师监督的技术,学习方法是竞争的,但(LVQ (Learning Vector quantization) algorithm: it is an extended form of supervised learning of Kohonen, which combines self-organizing and supervised techniques. The learning method is competitive, but the)

本代码是描述图像融合的一个综合性代码，其中包括对图像变换提升等一系列试验方案(The code is described in an integrated image fusion of code, including the transformation of images, such as a series of pilot programs to enhance)

说明：  图片的平滑锐化以及二值化。。。。。。。。。。。。。。。(Sharpening and smoothing the image binarization)