image fusion matlab code

说明：  基于FPGA平台的双目视觉处理项目，可以通过双目摄像头实现目标物体测距，测量大小等功能(Binocular vision processing based on FPGA platform)

1.设计一个程序，对一幅灰度图像的实现如下几何变换,1)放大为原图1.5倍 2)绕中心旋转30度(CW) 3)采用偏移量插值实现一个透视变换. 灰度差值用最近邻插值和双线性插值 2. 绘制一幅灰度图像的梯度幅度图像（三点法求梯度），针对梯度幅度图像合理的选择一个阈值（通过试验即可）将其二值化，以获得图像边缘检测图像。 ((1) design a program, a grayscale image as geometric transformation, zoom 1) for 1.5 times the original 2) around the center of rotation of 30 degrees (CW) 3) interpolation of the offset to achieve a perspective transformation.gray level difference between the nearest neighbor interpolation and bilinear interpolation (2) draw a grayscale image of the gradient magnitude image (three-point method seeking gradient), the gradient magnitude image is reasonable to select a threshold value (the test can be) the second value to the image edge detection image.)

说明：  小波变换是图像的多尺度、多分辨率分解，它可以聚焦到图像的任意细节，被称为数学上的显微镜。近年来，随着小波理论及其应用的发展，已将小波多分辨率分解用于像素级图像融合。把两个正交偏振的图片融合(Wavelet transform is the multi-scale and multi-resolution decomposition of an image. It can focus on any detail of the image and is called the mathematical microscope.In recent years, with the development of wavelet theory and its applications, wavelet multi-resolution decomposition has been applied to pixel-level image fusion)

实现单尺度retinex算法，用于图像的对比度增强，对低能见度图像处理效果较好。(Retinex single-scale algorithm for image contrast enhancement, image processing on low visibility is better.)

BlurTemplate.m为模糊模板函数子程序 DIPexp2.m为用逆滤波和维纳滤波进行恢复的源程序(Template function for the fuzzy BlurTemplate.m Subroutine DIPexp2.m using inverse filtering and Wiener filtering for the restoration of the source)

说明：  快速磁共振成像领域的GRAPPA（generalized auto-calibrating partially parallel acquisitions）算法实现(GRAPPA realization in fast MRI)

将没有exif文件信息的裸RAW数据转换成YUV422格式的数据.(Exif file information will not be the bare RAW data into YUV422 format.)

Viola-Jones Fast Face Detection的matlab实现。(Viola-Jones Fast Face Detection Mex Implementation)

利用霍夫变换检测直线，园，椭圆，曲线，简单有效，用用方便(Using Hough transform to detect straight lines, park, oval, curve, simple, effective, easy to use with)

利用卷积神经网络进行特征提取，并将卷积特征可视化(Feature extraction using convolutional neural networks and visualization of convolution features)