SpectralResidual

图像处理c源码_功能是打开一幅位图_并显示在屏幕上(_ C source image processing function is to open a bitmap _ and displayed on the screen)

说明：  像素与图像基本指标—彩色图像的基本信息、像素颜色值、图像的大小、矩阵形式等、对灰度图像计算有关统计参数：（1）图像的大小；（2）图像的灰度平均值；（3）图像的自协方差；（4）图像的灰度标准差、对图像进行45度，19度，98度等任意角度的旋转，并计算原图与旋转后图像的相关系数和协方差矩阵(Pixel and Basic Index of Image)

用来读写高光谱遥感影像的matlab代码，支持BSQ,BIL,BIP格式，三种文件格式对应的是头文件*.lxw中的第5个数字，头文件可用写字板编辑(The matlab code used to read and write in Hyperspectral Imagery support BSQ, BIL, BIP, format, corresponding to three types of file formats is a header file*. Lxw in the first five digits, the first file available WordPad editor)

带色彩恢复因子的retinex算法，用于处理雾天图像(Recovery factor with color retinex algorithm, used to deal with fog image)

读取RAW格式文件，将其转换为BMP格式文件并存储(raw 2 bmp file format convert)

基于图像检索的matlab代码，基于内容的图像检索中的一些关键环节：特征提取：颜色直方图；纹理特征等 相似度：马氏距离，欧氏距离等 相关反馈：机器学习方法，如SVM，神经网络等 检索与分类：两个很相似的样本距离很小，虽然两个不相似的样本距离未必很大(content-based image retrieval of some of the key issues : Feature Extraction : color histogram Texture characteristics of similarity : Mahalanobis distance, the Euclidean distance relevance feedback : machine learning methods, such as SVM. Neural network search and classification : two very similar samples from the small, although the two are similar to the samples may not be much distance)

对于低水平的视觉情况下，加权核规范最小化算法及其应用。(Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision)

分析了纹理特征提取方法，特别是灰度共生矩阵的方法和 Gabo:小波矩的方法。并在这两种方法的基础上提出了综合灰度共生矩阵和 Gbaor小波矩的纹理特征提取方法并用于图像检索。 (matlab)

区域生长算法，通过设置种子点来实现,代码注释完整(region grow algorithm with matlab)

2-band discrete wavelet transform (DWT) Dual-Tree Complex Wavelet Packet(The 2-band discrete wavelet transform (DWT) provides an octave-band analysis in the frequency domain, but this might not be ‘optimal’ for a given signal. The discrete wavelet packet transform (DWPT) provides a dictionary of bases over which one can search for an optimal representation (without constraining the analysis to an octave-band one) for the signal at hand. However, it is well known that both the DWT and the DWPT are shift-varying. Also, when these transforms are extended to 2-D and higher dimensions using tensor products, they do not provide a geometrically oriented analysis. The dual-tree complex wavelet transform (DT-CWT), introduced by Kingsbury, is approximately shift-invariant and provides directional analysis in 2-D and higher dimensions. In this paper, we propose a method to implement a dual-tree complex wavelet packet transform (DTCWPT), extending the DT-CWT as the DWPT extends the DWT. To find the best complex wavelet packet frame for a given signal, w)