united1

MPPT algorithm INC method

求灰度共生矩阵及其各要素用matlab实现(For gray-scale co-occurrence matrix and its elements realize with matlab)

对遥感图像进行分类，使用k均值算法,对Indian_pines数据进行分类(Remote sensing image classification, using the k-means algorithm)

用自相关法 来实现图像匹配，非常好用，值得一试(Autocorrelation method to achieve the image matching, very easy to use, it is worth a try)

可对灰度及彩色图像进行压缩及解压缩，通过变量名可以实现基于不同方式（多级树集合分裂spiht，ezw,stw,wdr等）的压缩，形如wcompress( c ,X,SAV_FILENAME,COMP_METHOD)(The wcompress command performs either compression or uncompression of grayscale or truecolor images.)

说明：  K-SVD matlab 源程序 里面包含了许多子函数(The matlab source program contains many subfunctions)

说明：  二值化算法，含otsu,niblack,kapur,kittlerMet等方法(Binarization algorithm, including otsu, niblack, kapur, kittlerMet and other methods)

数字水印处理，嵌入、提取、认证，可以在图形，视频和文本中添加数字水印，增强文档的保密，防止泄密(Digital watermarking processing, embedding, extraction, authentication, can be in graphics, video and digital watermarking to add text to enhance the confidentiality of documents to prevent leak)

针对可见光与红外图像的特点和难点，提出了可见光与红外图像配准与融合中的关键技术，即: 使用新型的基于一维最大类间方差和最大连通性测量的图像分割方法对源图像进行分割来更好地实行图像粗 配准 使用新型的特征点提取方法，特征点的匹配及误匹配的消除来更好地实行图像精配准 采用新型的基 于区域的树状小波活性测度计算来实现树状小波图像融合 利用自生成神经网络来实现模栩图像融合. (For visible light and infrared images of the characteristics and difficulties, a visual and infrared image registration and integration of key technologies, namely: the use of new one-dimensional based on the largest variance between-class connectivity and the largest measuring method of image segmentation source image segmentation to better implementation of coarse image registration the use of a new type of feature point extraction method, feature points of the match and the elimination of false matches to better the implementation of image fine registration the use of new tree-based wavelet calculated to measure the achievement of the activity tree wavelet image fusion the use of self-generating neural network to achieve image fusion Xu mode.)

Zhang等人 利用相位一致性信息的特性，提出了 特征相似性方法(FSIMG) ，选取了相位一致性信息和梯度信息 作为它的两个特征，得到了较好的结果。 (A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS’ perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA s demonstrate that FSIM can achieve much higher consistency with the subjective uations than state-of-the-art IQA metrics.)