WOW_matlab

一幅大小为256×256的256级灰度图像（每像素8bit） 如果图像的各个像素之间是统计独立的，求该图像的信息熵 -离散无记忆信源 如果图像的各个像素之间不是统计独立的，求该图像的信息熵 -图像像素的概率分布模型(A 256 gray scale image (8bit per pixel) with a size of 256 x 256 If each pixel of the image is statistically independent, the information entropy of the image is calculated Discrete memoryless source If each pixel of the image is not statistically independent, the information entropy of the image is calculated Probability distribution model of image pixels)

说明：  小波分析增强滤波。小波变换的原理类似傅里叶变换，只是把三角函数基换成了小波基。与傅里叶变换不同，小波变换有两个变量：scale和translation。scale控制小波函数的收缩，其导数即为频率，translation控制小标函数的平移，平移量对应时间。通过信号的伸缩平移，可以得到某种重合情况，这样积分也会得到一个极大值，不同的是，得到频率成分的同时，还可以知道该频率的时间位置。(Wavelet analysis enhances filtering.The wavelet transform works like the Fourier transform, except that the trigonometric basis is replaced by a wavelet basis. Unlike the Fourier transform, the wavelet transform has two variables: scale and Translation. Scale controls the contraction of the wavelet function, its derivative is frequency, translation controls the translation of the small scale function, and the translation corresponds to the time. By stretching and shifting the signal, you can get some kind of coincidence, so that the integral will also get a maximum, but the difference is that when you get the frequency component, you can also know the time position of that frequency.)

分数阶各向异性扩散图像去噪，实现了文献Fractional-Order Anisotropic Diffusion for Image Denoising, Jian Bai, Xiang Chu, IEEE T. Image Process.,2007, 16(10): 2492-3502(Fractional anisotropic diffusion denoising literature Fractional-Order Anisotropic Diffusion for Image Denoising, Jian Bai, Xiang Chu, the IEEE T. Image the Process, 2007, 16 (10): 2492-3502)

一些常见的图像处理方面的图像（尺寸不一）(Some common image processing aspects (different sizes))

一个改进的卡尔曼滤波器 用来进行运动目标的跟踪 (An improved Kalman filter used for tracking moving targets)

说明：  图像分割代码，c++实现(Image segmentation code, c++ Achieve)

说明：  对图像进行插值，以提高分辨率，计算效率较高(The image is interpolated to improve the resolution)

常规的中值滤波器，在噪声的密度不是很大的情况下效果不错。但是当概率出现的概率较高时，常规的中值滤波处理后，仍然具有噪声点，并丢失了细节和边缘，效果不是很好。 自适应中值滤波器可以尽可能的保护图像中细节信息，避免图像边缘的细化或者粗化。(The conventional median filter works well when the noise density is not very large. However, when the probability of probability is high, the conventional median filter still has noise points, and the details and edges are missing. The result is not very good. The adaptive median filter can protect details in the image as far as possible and avoid thinning or coarsening of the image edges.)

详细介绍PCNN算法用于lena图像分割的实例，并用matlab实现(Detailed the PCNN algorithm for instance lena image segmentation and implemented using matlab)

SAR图像理解与解译研究进展,电子学报,(SAR image understanding and interpretation of research)