xiaobo53

说明：  基于小波变换，小波分解的图像融合算法，完整代码(Image fusion algorithm based on wavelet transform and wavelet decomposition, complete code)

图像校正的Matlab代码，非常不错，可以实现很好的校正结果，速度也很快(Image correction of Matlab code, very good, very good correction can be achieved the result of quick)

智能图像处理提出了一种改进的基于特征和回归分析的人群密度估计算法。引入图像中的角点个数作为低层特征(Interactive Images: Cuboid Proxies for Smart Image Manipulation)

一种基于CDD的图像修复算法，效果还不错，做图像修复的可以好好看看，还有论文哦 (Based CDD image restoration algorithm, the results were pretty good, do image restoration can be a good look, there are papers Oh)

根据RBM深度网络（Hinton，2006）进行MINST手写文字的分类器的训练，利用训练得到的权值制作了这个小程序，通过这个程序可以看出训练结果对数据集内的测试样本和训练样本都能进行很好的识别，但是对其他的手写字体识别就没有那么好了。(According to RBM depth network (Hinton, 2006) conducted MINST handwritten text classifier training, using the training to get the right value to produce this small program, this program can be seen through the results of the training data set and the training sample test samples are can be well identified, but on the other handwriting recognition is not so good.)

算术迭代算法ART ct 重建算法的vc工程(ART CT reconstruction)

说明：  压缩感知稀疏恢复中使用的OMP算法，matlab例程(OMP algorithm and MATLAB routine used in compressed sensing sparse recovery)

RPCA (Robust Principal Component Analysis)是目前用于矩阵填充、图像去噪的最有效的优化方法。目前最有效的算法是ALM（Augmented Lagrange Multiplier）。ALM分为Exact ALM和Inexact ALM。 该代码是Inexact ALM，收敛速度比Exact ALM快！(RPCA (Robust Principal Component Analysis) is used for matrix filling, image denoising. It is currently the most effective optimization method. Currently the most effective method is ALM (Augmented Lagrange Multiplier). There re 2 kinds of ALM: Exact ALM and Inexact ALM. The code is Inexact ALM, faster convergence speed than the Exact ALM!)

说明：  用于医学图像融合CT和MRI灰度图像，MRI和PET的彩色图像融合(For medical image fusion)

针对粒子概率假设密度(PH D)滤波算法在虚警、漏检情况下，b标状态估计不稳定和b标可观测性车弱的问题，提出了一种基于序贯融合的粒子PH D滤波方法，利用雷达和红外传感器多目标进行融合跟踪杏基本思想是先对红外传感器进行粒子PH D滤波，丙将红外传感器滤波结果作为雷达的预测值，然后利用P达观测的数据进行更新，这样通过雷达和红外传感器交替上作保证目标状态的可观测性，从滤波器输出结身即可得到目标的状态信息仿真结果表明，在虚警、漏检和密集目标环境下，该方法是有效的和稳健的(The problem of target state estimation instability and observability weakerin the presence offalse alarms and missed detection was deal with. On the basis of sequential fusion, a particleprobabil- ity hypothesis density(PHD) filter for multrsensor multrtarget tracking was proposed. Observed da to collected from the infrared sensor was estimated with the particle PHD filter. Then the results from the filter were set as the radar predicted value by the radar observations. The multrtarget state can be updated to guarantee observing the target state. In this way, the global state is updated at the fusion center. Simulation results show that the proposed algorithm is effective and robust under the false warning, omission and concentrated target environment. )