去噪评价指标ENL, SSIM, PSNR,SNR,EPI

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)

图像传感器，TSC3200D，经典颜色识别的传感器资料。源自美国TAOS公司，光电矩阵元件，具有多种彩色识别功能。(TCS3200, TCS3210 PROGRAMMABLE COLOR LIGHT-TO-FREQUENCY CONVERTER USA TAOS)

本文首先对数字全息技术的记录和再现的基本原理、数值重建算法、频谱滤波方法，数字全息图的记录条件、再现光场的分离条件等基本问题进行了分析讨论(Firstly, the basic principle of the recording and reproducing of the digital holography, numerical reconstruction algorithm, the spectral filtering method, the digital hologram recording conditions, reproducing conditions of separation of the light field, and other basic issues discussed)

1、对观测数据进行中心化，； 2、使它的均值为0，对数据进行白化—>Z； 3、选择需要估计的分量的个数m，设置迭代次数p<-1 4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）； 5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的） 6、用对称正交法处理下W 7、归一化W(:,p)=W(:,p)/norm(W(:,p)) 8、若W不收敛，返回第5步 9、令p=p+1，若p小于等于m,返回第4步 剩下的应该都能看懂了 基本就是基于负熵最大的快速独立分量分析算法(1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm)

细菌觅食算法在人脸识别中的应用，图像经过小波变换预处理(The application of bacterial foraging algorithm in face recognition, image preprocessing by wavelet transform)

本算法主要采用变分贝叶斯求解图像忙恢复问题。(This package contains implementations of the MAP_k blind deconvolution algorithms described in the paper "Efficient Marginal Likelihood Optimization in Blind Deconvolution" Levin, Weiss, Durand and Freeman, )

说明：  高光谱遥感图像光谱解混的独立成分分析技术(Independent component analysis technique for spectral unmixing of hyperspectral remote sensing images)

图像分割源程序 通过融合处理，％ image segmentation based on multispectral image fusion ％ and morphology reconstruction watershed ％ main program (image segmentation source through fusion, % image segmentation based on multispectral im age fusion% and morphology reconstruction wat ershed% main program)

matlab code for processing of signals and images

何凯明暗原色去雾方法的matlab程序，按此种方法进行图像去雾(He Kaiming dark color to the fog method matlab program, according to this method the image to fog)