FAST-ICA

1 完成图象处理的基本功能2：二值化、边缘检测、滤波。 2 熟悉dc的部分常用功能，学习图标控制弹出框。(a complete image processing of the two basic functions : values, edge detection, filtering. 2 familiar with the dc part of the commonly used function, learning to control pop icon frame.)

利用自编的mean shift对图像进行均值漂移分割。(The mean shift use self mean shift image segmentation.)

说明：  对图像进行RGB、HSV单通道分离，滤波，合并。(Single-channel filtering and merging of images)

使用ROEWA算子对有噪声SAR图像进行边缘检测(Use ROEWA operator of noise SAR image edge detection)

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.)

一个基于Tikhonov正则化超分辨率重建，可以成功运行。(A Tikhonov based regularized super resolution reconstruction can be successfully operated.)

实现图像的分块压缩感知重建，DCT变换，OMP重构算法(Image block compressed sensing reconstruction, DCT transform OMP reconstruction algorithm)

CVPR2013中出现的一种显著性特征检测算法，该算法用于模拟人眼视觉识别过程，提取图像中的显著性特征借后期目标对象的检测跟踪等使用，该算法(Context-Aware Saliency Detection。matlab implementation of an classical saliency detection alrogithm,which imitate our recognition process of eyes)

自己编的加噪声的 很好用哦(Own noise increases by Oh well)

MATLAB实现的 人脸检测定位,M文件,较为准确的(MATLAB realize Face Detection positioning, M files, more accurate)