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Matlab下，读取raw文件，实现多波段图像融合，高光谱与高分辨率图像融合，包括直接融合，傅里叶变换后融合，小波变换后融合及HIS变换后融合。(On Matlab,how to read and show raw files, achieve more band mergering,spectrum image and high resolution image fusiing,including direct fusion,Fourier transform fusion, wavelet transform fusion and HIS transformation fusion )

这个程序可以用于两个图像的拼接，通过寻找特征点完成拼接(This program can be used to stitch two images and complete the stitching by finding feature points.)

图像的二值化，并对图像做膨胀，腐蚀，开、闭操作，以及细化处理等通过用matlab高级语言编写数字图像处理的一些基本算法程序(Binarization image, and image to do the expansion, corrosion, open and close operation, as well as refining processing through high-level language matlab digital image processing procedures for some of the basic algorithm)

非常有用的平滑l0算法代码程序，实现信号的稀疏重构(Very useful for smoothing the l0 algorithm code procedures to achieve signal sparse reconstruction)

针对杂波环境下的多个机动目标跟踪问题, 本文将多模型概率假设密度 (Multiple-model probability hypothesis density, MM-PHD) 滤波器和平滑算法相结合, 提出了 MM-PHD 前向 – 后向平滑器. 为了避免引入复杂的随机有限集 (Random finite set, RFS) 理论, 本文根据 PHD 的物理空间 (Physical space) 描述法推导得到了 MM-PHD 平滑器的后向更 新公式. 由于 MM-PHD 前向–后向平滑器的递推公式中包含有多个积分(By integrating the multiple-model probability hypothesis density (MM-PHD) filter with the smoothing al- gorithms, an MM-PHD forward-backward smoother is proposed in this paper for tracking multiple maneuvering targets in clutter. To avoid use of complex random finite set (RFS) theory, the backward updated equation of the MM-PHD smoother can be derived according to the physical-space explanation of the PHD)

人耳识别，包含图像预处理，图像特征点的提取，比较实用(Ear recognition)

说明：   最简单的差分格式有向前、向后和中心3种。 向前差分：f (n)=f(n+1)-f(n) 向后差分：f (n)=f(n)-f(n-1) 中心差分：f (n)=[f(n+1)-f(n-1)]/2(The easiest difference format forward, backward, and three kinds of centers. Forward differencing: f (n) = f (n+ 1)-f (n) Backward difference: f (n) = f (n)-f (n-1) Central difference: f (n) = [f (n+ 1)-f (n-1)]/2)

说明：  图像融合常见功能，3类共12种图像融合算法，GUI界面，源图像(Common functions of image fusion, 3 types of 12 image fusion algorithms, GUI interface, source image)

houph变换，通过梯度函数得到图像的梯度矩阵，通过检测圆检测眼睛位置。(houph transform, through the gradient function to be the image gradient matrix, circle detection by detecting the location of the eyes.)

赵巨峰，陶小平，*冯华君，徐之海，李奇, 基于梯度的波纹图像质量评价 [J], 浙大学报工学版, 2010, 44(5):870-874.(Gradient based ripple image quality assessment)