Gauss-domain-time-frequency--

dtcwt 双树复小波的matlab工具箱,(dtcwt dual tree complex wavelet Matlab toolbox,)

彩色图像处理 格式转化rgb2hsi，hsi2rgb 冈萨雷斯(AN M-function fro converting from RGB TO HSI, HSI to RGB)

请采用学过的图像去噪方法（鼓励自己研究新算法），对Circuit_noise.jpg 、boy_noisy.gif（周期噪声） 和california_22_13.bmp图像进行去噪处理。(Please use learned denoising method (to encourage their study new algorithm), to Circuit_noise.jpg, boy_noisy.gif (periodic noise) and california_22_13.bmp image de-noising.)

经典ldpc编译码程序 gallager的博士论文里的部分仿真程序,(Classic LDPC decoding programming gallagher s doctoral thesis in part of the simulation program,)

基于MSRCR和SSR算法实现一个基本的图像去雾程序(A basic image fogging program based on MSRCR and SSR algorithm)

快速角点检测算法，本算法针对图像中存在的线和点进行检测，算法通过matlab程序实现，结果表明具有很好的效果(Fast corner detection algorithm, the algorithm for images that exist in the line and point detection algorithm matlab program, results show that with good results)

说明：  注：运行前自己准备图像，修改I = imread('')单引号中的路径 1. 给一张灰度图添分别添加高斯噪声和椒盐噪声。然后分别进行均值滤波和中值滤波，并把处理后的图片保存。 2. 图像频域处理，对图像进行傅里叶变换，进行高通滤波，显示处理结果。 3. 图像平移和旋转 4. 分别用OSTU阈值法和全局阈值法分割图像，然后进行腐蚀和膨胀处理，显示处理结果。(Note: Prepare the image by yourself before running, modify the path in single quotes of I = imread('')1. Add Gaussian noise and salt and pepper noise to a grayscale image. Then perform mean filtering and median filtering respectively, and save the processed pictures.2. Image frequency domain processing, Fourier transform of the image, high-pass filter, and display the processing result.3. Image translation and rotation4. Separate the image with OSTU threshold method and global threshold method, and then perform corrosion and dilation processing, and display the processing results.)

有色噪声干扰非平稳状态下，MVDR扩展凹槽滤波形成方法(With colored noise under non-steady state, the the MVDR extended groove filter forming method)

同态滤波器 作用：消除不均匀照度的影响, 增强图象细节。(Filter function with the state: to eliminate the impact of uneven illumination, enhanced image detail.)

对场景分类和语义特征稀疏化的高层图像表示(Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification)