小波分解图及图片

Level Set Methods and Dynamic Implicit Surfaces - Ronald Fedkiw(Level Set Methods and Dynamic Implicit Surfaces- Ronald Fedkiw)

主要包括3种去噪方法，Bayesian Least Squares，BM3d 和Non-local means (this attachment contains three classical denoising methods Bayesian Least Squares，BM3d and Non-local means )

这是本人开发的一个课题软件，主要用于点细胞识别与轨迹追踪! 具有 很好的研究价值，里面运用 图像处理（很多种）函数的 编写与调用！！ 估计仅此一个项目代码，版主就应该给我特殊鼓励的！(err)

利用分数阶傅里叶变换的方法，通过迭代相位将加密图像替换为所需要的相位达到加密效果，上传压缩包包括代码和图片，不需要任何修改即可运行。(Using a fractional Fourier transform, the iterative phase replaces the encrypted image with the required phase to achieve the encryption effect. Uploading the compressed package includes the code and picture, and it can be operated without any modification.)

视频转帧预处理 ，应用于红外图像序列处理，为后续处理提供便宜。(Video frame preprocessing, applied to infrared image sequence processing, provides cheaper for subsequent processing.)

说明：  LBP模式下的人脸识别方法，matlab范例，有论文说明，LBP算法(LBP mode of face recognition method, matlab example, there are papers, LBP algorithm)

蚁群算法的另一种实现形式,通过全局的搜索,避免陷入局部解的改进程序(Another implementation form of ant colony algorithm, through global search, avoid falling into an improved program of local solution.)

说明：  虚拟内窥镜关键技术研究 虚拟内窥镜技术（Virtual Endoscopy）是虚拟现实技术在现代医学中 的应用。它利用医学影像作为原始数据，融合图像处理、计算机图形学、科 学计算可视化、虚拟现实技术，模拟传统光学内窥镜的一种技术。它克服了 传统光学内窥镜需把内窥镜体插入人体内的缺点，是一种完全无接触式的检 查方法。虚拟内窥镜的研究旨在为医生提供诊断依据，还可应用于辅助诊断、 手术规划、实现手术的精确定位和医务人员的培训等。(Research on key technologies of virtual endoscopy virtual endoscopy (Virtual Endoscopy) is a virtual reality technology in modern medicine. It has made use of medical imaging as an original data, the integration of image processing, computer graphics, scientific visualization, virtual reality technology to simulate a conventional optical endoscope technology. It overcomes the traditional optical endoscope to be inserted into the endoscope body of the shortcomings of the human body is a no-contact inspection method. Virtual Endoscopy of the study was to provide diagnosis for doctors, but also applies to diagnosis, surgical planning, to achieve precise positioning of surgical and medical personnel training.)

It initializes the entire image processing. The simulation parameters can be changed in this main file.

基于字典学习和矩阵低秩稀疏分解的SAR图像处理新方法，拓展了低秩稀疏分解的应用领域，有很好的应用前景。(Dictionary Learning and Low-rank Sparse Matrix Decomposition for Sparsity-driven SAR Image Reconstruction)