SVD

标记分水岭比较了开闭和基于重构的开闭的效果及分割染色后的效果图... 标记分水岭分割算法(Mark a watershed in comparing the opening and closing and effect diagram based on the effect of opening and closing and split reconstructed stained ... marked watershed segmentation algorithm)

近期成果，与大家分享：奇异值分解、小波降噪、zoom细化 谱峭度 和滤波器设计，共勉(Recent achievements to share with you: singular value decomposition, wavelet noise reduction, zoom refined spectral kurtosis and filter design, encouragement)

基于相似度的图像质量评价算法Structural SIMilarity (SSIM) index的实现。(This is an implementation of the algorithm for calculating the Structural SIMilarity (SSIM) index between two images. Please refer to the following paper: Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity" IEEE Transactios on Image Processing, vol. 13, no. 4, pp.600-612, )

编制通用的二值形态学运算（腐蚀和膨胀、开、闭和击中击不中变换）函数，函数可以处理不同的变换形式和不同类型的结构元素。并用你编制的形态学函数对一个二值图象进行处理。 用形态学击中击不中变换方法对图象进行细化处理。 实验图象：二值图象由一段文字组成，word_bw.bmp (The preparation of binary morphological operations (erosion and dilation, opening, closing and hit-miss transform) function, the function can handle the transformation of different forms and different types of structural elements. You prepared in morphology function of a binary image. Morphological hit-miss transform method of image refinement process. Experimental images: binary image by a paragraph of text, word_bw.bmp)

通过稀疏重构一幅自然图像，使得该图像重新生成，同时找出稀疏系数。(One kind of denoising signal sparse representation)

图像处理中的数字识别代码，包括数字训练集合和训练分类算法，可以实际应用。(digital recognition)

说明：  用Lorenz序列进行彩色图像加密，加密效果良好，性能得到较大提升(Using Lorenz sequence to encrypt color image, the encrypting effect is good and the performance is greatly improved.)

根据图像色彩信息，寻找图像的质心，以便对目标的跟踪(The image color information, look for the image of the center of mass to the target track)

国外最近的图像密写论文，提出了YASS以及YASS的改进(Recent image abroad, write the paper closely, and YASS YASS proposed improvements)

高光谱图像MNF算法，实现高光谱图像的去噪，注释详尽(Hyperspectral image MNF algorithm to achieve denoising hyperspectral images, detailed comments)