rotate_blur

增强型Lee滤波，附有详细注释，简单易用，参数可调(enhanced LEE filter)

说明：  当前论文主要考虑的是非信号依赖的高斯噪声下的图像恢复，本程序实现了泊松噪声下的图像恢复，泊松噪声为信号依赖噪声，能够更加有效逼近实际成像系统噪声。( This is the code that was used in the papers "A Nonnnegatively Constrained Convex Programming Method for Image Reconstruction", "Total Variation-Penalized Poisson Likelihood Estimation for Ill-Posed Problems", "Tikhonov Regularized Poisson Likelihood Estimation: Theoretical Justification and a Computational Method", "An Efficient Computational Method for Total Variation with Poisson Negative-Log Likelihood", "An Analysis of Regularization by Diffusion for Ill-Posed Poisson Likelihood Estimation," "An Iterative Method for Edge-Preserving MAP Estimation when Data-Noise is Poisson", and finally, "Regularization Parameter Selection Methods for Ill-Posed Poisson Maximum Likelihood Estimation". See my publications page for more details. The main algorithm is for nonnegatively constrained, regularized Poisson likelihood estimation. At this point you can choose Tikhonov, total variation regularization, and diffusion regularization. A number of other methods are also implemented. Regularizatio)

将处理过的二进制比特流写入图像，保存显示 (The treated binary bit stream to write the image )

数字图像处理课程期终项目，对图像进行放大，并进行滤波，达到一定的信噪比。(The final project of the digital image processing lesson, which is programmed for enlarging a image into demanding size.)

多传感器多目标跟踪技术研究，，主要研究多传感器多目标跟踪技术 (Multi-sensor multi-target tracking technology research, mainly multi-sensor multi-target tracking technology)

这是一个matlab环境下的关于图像频谱分析的源代码，内附图像。(This is a Matlab environment on the image spectrum analysis of source code, containing images.)

利用surf算法,来实现图像间的拼接,并可将其推广为多幅图之间的拼接融合。(Realization of two images splicing)

萤火虫算法 GSO算法用matlab编程实现(Firefly algorithm GSO algorithm using matlab programming)

说明：  在这个知识库中，图形信号处理的一些有趣的特性被表示出来。演示通过经典信号处理和图形信号处理对一维和二维欧几里得域信号应用低通滤波器，比较两种方法的结果。在此基础上，验证了图形信号处理的工作机制。此外，还描述了在非欧几里得域上滤波信号的示例。(In this repository, Some fascinating features of Graph Signal Processing were represented. Demos incudes applying a low-pass filter on both 1D and 2D euclidian domain signal by classical signal processing and also Graph signal processing to compare both results are the same. Within that way, we will validate the working mechanism of Graph Signal Processing. In addition to this, an example of filtering a signal on the non-euclidian domain was also represented.)

contourlet对LENA512灰度图的LP,DEF分解与系数重构代码,经过调试.(Contourlet LENA512 grayscale of the LP, DEF coefficient decomposition and reconstruction of the code after debugging.)