plsa

锐化图片 拉普拉斯算法 两种方法 一种是自己写的一种是用了matlab里面自带的函数(ruihua picture Laplace there are two mechof a fi [as fs aflasl;nvb)

应用灰度共生矩阵求解纹理特征并用熵，相关性，惯性矩，能量，局部平衡等参数带有实例照片可以直接编译(Applying the grayscale symbiotic matrix to solve the texture features and using entropy, correlation, inertia moment, energy, local equilibrium and other parameters with example photos can be compiled directly.)

说明：  LK光流法算法，基于LK光流法的Matlab算法，本人自编(LK Optical Flow In Matlab)

基于matlab的CT图像处理，提取感兴趣的区域，并染色。计算面积(Based on matlab CT image processing, extract the region of interest and dyed. Calculate the area)

N年前上数字图像课时写的实验报告和程序，用滤波反投影（Filter Back Project，FBP)法重建图像。(Report and source code for data image restore experiment, using FBP.)

将RGB格式的图片进行mosaic处理得到RawData格式的图片，然后再恢复成RGB格式彩色图片,laroche-CFA插值算法(turn an RGB picture to RawData picture，then recover the RawData picture to an RGB picture.it is called laroche-CFA.)

利用最大类间方差算法求取自动阈值，实现图像分割(Using the maximum inter class variance algorithm for automatic threshold, image segmentation)

说明：  SIFT，即尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点，是一种局部特征描述子。 该方法于 1999 年由 David Lowe 首先发表于计算机视觉国际会议（International Conference on Computer Vision，ICCV），2004 年再次经 David Lowe 整理完善后发表于 International journal of computer vision（IJCV） 。截止 2014 年 8 月，该论文单篇被引次数达 25000 余次。(来自百度)(SIFT, namely Scale-invariant feature transform (SIFT), is a description used in the field of image processing. This description has scale invariance and can detect key points in images. It is a local feature descriptor. This method was first published by David Lowe at the International Conference on Computer Vision (ICCV) in 1999 and published in the International Journal of Computer Vision (IJCV) in 2004 after it was sorted out and perfected by David Lowe again. As of August 2014, more than 25,000 citations have been cited. (From Baidu))

图像的采样 • 写程序：以2 为尺度，下采样图像 ，观察不同分辨率下图像的变化情况(a) • 写程序：把(a)中获得的图像，进行上采样处理，可以选择不同的平滑方 法，比如，邻近像素复制，或者线性插值等。讨论上采样后图像的质量变 化。找出相应的改进方法。

de-speckling of noise in medical ultra sound images...