LMS

叠加平均去噪过程的实现，信号产生，加噪，去噪效果的显示(Average denoising, signal generation, noise addition, display the denoising effects)

近似熵算法对脑电信号进行特征提取的一些材料(Some materials approximate entropy algorithm for EEG feature extraction)

matlab环境下的基于形态学的分水岭算法,进行图象分割.(Matlab environment based on the morphology of the watershed algorithm for image segmentation.)

说明：  Python 分水岭算法 用于图像分割等图像处理(Python watershed algorithm)

圖像打印

说明：  当前论文主要考虑的是非信号依赖的高斯噪声下的图像恢复，本程序实现了泊松噪声下的图像恢复，泊松噪声为信号依赖噪声，能够更加有效逼近实际成像系统噪声。( This is the code that was used in the papers "A Nonnnegatively Constrained Convex Programming Method for Image Reconstruction", "Total Variation-Penalized Poisson Likelihood Estimation for Ill-Posed Problems", "Tikhonov Regularized Poisson Likelihood Estimation: Theoretical Justification and a Computational Method", "An Efficient Computational Method for Total Variation with Poisson Negative-Log Likelihood", "An Analysis of Regularization by Diffusion for Ill-Posed Poisson Likelihood Estimation," "An Iterative Method for Edge-Preserving MAP Estimation when Data-Noise is Poisson", and finally, "Regularization Parameter Selection Methods for Ill-Posed Poisson Maximum Likelihood Estimation". See my publications page for more details. The main algorithm is for nonnegatively constrained, regularized Poisson likelihood estimation. At this point you can choose Tikhonov, total variation regularization, and diffusion regularization. A number of other methods are also implemented. Regularizatio)

用偏微分方法进行图像修复，可以修复灰度和彩色图像(By partial differential method for image restoration, can repair gray and color images)

图像去噪,应用DTCWT 进行地震信号的去噪和重建(picture-denosing apply DCTWT into signal of earthquake denosing)

说明：  都是各向异性扩散用于图像去噪的文章，中文文献，看起来比较方便(Are anisotropic diffusion for image denoising article, Chinese literature, it seems more convenient)

说明：  这是一个包括bmp文件显示，图像二值化，细线化等集合的程序，可在tc中运行。 程序中有40 +的注释，你能很快的看懂程序(This is a bmp file, including display, image binarization, thinning and other collection procedures, can be run in the tc. 40 + program notes, you can quickly understand procedures)