PCA_change_detecttion

基于SURF算法的图像拼接程序，执行效率高于SIFT算法(Executive efficient than SIFT algorithm based on the the SURF algorithm of image stitching procedure)

此程序完成了SSR算法，实现的对比度下的医学图像增强。 可以实现的程序！(this procedure completed the SSR algorithm, and the contrast of the medical image enhancement. The procedure can be achieved!)

说明：  opencv基础教程，对于初学者来说是个不错的选择。(opencv basic tutorial for beginners is a good choice.)

遥感图像时空融合，朱孝林博士的作品，可以参考学习(Space-time Fusion of Remote Sensing Images, Zhu Xiaolin)

对图像切割成若干任意大小图像，并进行滤波平滑。(Cut image into little ones and make the little pictures more smoth ,so well ,ha ha ha .)

针对 Anrlod 变换的周期依赖于图像的阶数这一特性, 提出了一种反变换算法 。 该算法 通过分析加密图像任一点处两坐标分量间关系, 得到原图像相应点的坐标 , 从而实现图像的解密。(In view of the characteristic that the Anrlod transform cycle depends on the order of the image, an inverse transformation algorithm is proposed. The algorithm obtains the coordinates of the corresponding points of the original image by analyzing the relationship between the two coordinate components at any point of the encrypted image, so as to achieve the decryption of the image.)

说明：  基于MATLAB实现传统图像去噪算法（均值滤波、中值滤波、非局部均值滤波NLM、三维块匹配滤波BM3D）(The traditional image denoising algorithms (mean filter, median filter, NLM, 3D block matching filter BM3D) and dncnn image denoising algorithm based on deep convolution neural network are realized based on MATLAB.)

图像修复，使用曲率方法，热传递。边缘修复非常好，参考某博士论文写。(Image restoration, using the curvature method, heat transfer. Edge of the repair is very good, the reference of a doctoral dissertation.)

说明：  经典的粒子滤波算法，适用于粒子滤波的初学者更好的了解粒子滤波(Classical particle filter, particle filter for better understanding of particle filter for beginners)

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)