FUSION

基于8×8分块得数字水印嵌入和提取算法。此算法已经通过调试，可以直接运行(Based on 8 × 8 sub-blocks have digital watermark embedding and extraction algorithm. This algorithm has been through debugging, you can directly run)

实现运动模糊图像的非盲卷积 分区域去模糊 有用(Implementation of motion blurred image blind deconvolution is useful to fuzzy areas)

太阳光伏阵列的最大功率点跟踪算法（MTTP）(Solar photovoltaic array maximum power point tracking algorithm (MTTP))

说明：  toolbox_calib图像畸变矫正工具箱(Image distortion correction toolbox)

[SIAM]Nonlocal Active Contours该文件包含论文及代码，主要内容是在水平集图像分割中引入non-local的性质，构造能量泛函，进行图像分割(this file include both paper and code, the main idea is utilize the non-local peperty in level set method for image segmentation )

NIPS2006论文“Saliency based on Information Maximization”的源码，用于显著图计算，作者是N. Bruce 和 J.Tsotsos (a code for "Saliency based on Information Maximization",N. Bruce &J.Tsotsos ,NIPS,2006)

城轨车辆轮对踏面故障在线监测系统，基于振动检测法的轮对踏面故障监测系统，通过傅里叶变换、小波包分析对釆集到的振动信号进行分析获得信号的特征,研究了基于短时能量判断的踏面故障特征提取算法。(On line monitoring system for wheel tread of urban rail vehicles, based on the vibration detection method of wheel tread fault monitoring system, through the Fu Liye transform, wavelet packet analysis of the collected vibration signals to obtain the characteristics of the signal, the study of the short-term energy judgment based on the feature extraction algorithm.)

遥感影像可以用TIFF文件表示，为了读取遥感影像，可以使用python读取TIFF文件并进行重排(Remote sensing images can be represented by TIFF files. In order to read remote sensing images, TIFF files can be read and reshapeed by using Python)

说明：  道床加速度时程曲线。功率谱密度函数 图。1/3倍频程Z振级频谱图。道床Z振级VLz（dB）。(Acceleration time history curve of ballast. Power spectral density function. Frequency spectrum of 1 / 3 octave Z vibration level. Ballast Z vibration level vlz (DB).)

用fibonacci变换方法对图象做置乱或逆置乱，效率极高，在信息隐藏中属常用的方法之一。调用函数为f=fibonacci_t(I,r,s)，其中参数分别为I=被置乱的图像，r=置乱密钥，s=0置乱，s=1逆置乱(Fibonacci transformation method used to do on the image or inverse Scrambling Scrambling, high efficiency in the information hidden in one of the methods are commonly used. Call the function f = fibonacci_t (I, r, s), which parameters were I = have been scrambling the image, r = scrambling key, s = 0 Scrambling, s = 1 inverse Scrambling)