模极大值边缘检测-高斯噪声

检具设计工具 运行于ug3d设计软件环境。支持2.0以上。有些版菜单本会出现乱码(The tool design tool runs in the ug3d design software environment. Support more than 2. Some of the versions of the menu would have been messed up)

《数字图像处理（第三版）》，中文版，作者是（美）冈萨雷斯，本书是数字图像处理经典著作。("Digital Image Processing (3rd Edition)", Chinese Edition, by Gonzalez, USA. This book is a classic work of digital image processing.)

里面包括24位真彩色图像类和其基础处理的类，包括灰度化，二值化和形态学操作(Includes 24-bit true color image processing based on class and class, including the gray-scale and binary morphological operations and)

使用二維的傅立業轉換，在以反傅立業的方式，將結果以影像話方式呈現。(Industry use of two-dimensional Fourier transformation, anti-Fu Li industry in the way of results, then the way to show images.)

本程序是基于单幅图像的超分辨率算法，通过对输入的高分辨率图像进行下采样得 % 到原始低分辨率图像，对输入的低分辨率图像插值后，利用DWT将低分辨率图像分 % 解为不同的4个子带；同时直接对对分辨率图像进行SWT处理。由SWT得到的高频子 % 带来修正DWT得到的高频子带。最后，通过逆离散小波变换（IDWT）组合修正的高 % 频子带和低分辨率图像，得到输出的超分辨率图像。通过计算PSNR和SSIM评价结果。(This program is a super-resolution algorithm based on a single image, which is sampled from the high resolution image of the input. % to the original low resolution image, after interpolating the input low resolution image, the low resolution image is divided by DWT. The% solution is different 4 subbands, and the resolution image is processed by SWT. High frequency subsets obtained by SWT % brings the high frequency subband obtained by the modified DWT. Finally, the height of the combination of inverse discrete wavelet transform (IDWT) % frequency subband and low resolution image, and the output super-resolution image is obtained. The results were evaluated by calculating PSNR and SSIM.)

说明：  matching pursuit algrithmic

c sharp 图像识别的程序，包括灰度，二值化等方面的操作(c sharp)

matlab realtime 要求的环境：matlab (matlab realtime)

数字图像处理中关于色彩空间变换的实现，色彩空间包括：YUV，YCbCr,Iab,RGB,XYZ等等。(Digital Image Processing for color space conversion in the color space include : YUV, YCbCr, Iab, RGB, XYZ and so on.)

说明：  以稀疏基有离散余弦变换基（DCT）和快速傅立叶变换基（FFT）做为稀疏基，高斯随机矩阵、部分哈达玛矩阵为测量矩阵，L1范数、正交匹配追踪算法（OMP）为重建算法进行压缩感知算法实现。 以f = cos(2*pi/256*t) + sin(2*pi/128*t)做为原信号，取原信号f的20%做为输入进行压缩感知重建。(The sparse basis includes discrete cosine transform (DCT) and fast Fourier transform (FFT) as sparse basis, Gaussian random matrix and partial Hadamard matrix as measurement matrix, L1 norm and orthogonal matching pursuit algorithm (OMP) as reconstruction algorithm. In this paper, the reconstructed signal is reconstructed by using the original sensing signal (sinf * t) as the original input signal (i.e. sinf * t * 2) + 2 * s * t * as the input signal.)