image

说明：  simulation code matlab NOMA

基于NSCT的几种图像融合算法，并对算法的性能作了比较，该代码有详细的说明。(Several image fusion algorithm based on NSCT, and compares the performance of the algorithm, the code is described in detail.)

可以计算mtf，使用matlab输入图像即可实现计算(You can calculate MTF and use matlab to input images.)

采取逆滤波和维纳滤波两种图像恢复算法对退化图像的恢复实现过程。 (taken inverse filter and two Wiener filtering algorithm to image restoration of degraded images to achieve the restoration process.)

对于存在块效应图像进行质量评价 matlab程序 比较适合初学者(For the existence of blocking matlab image quality evaluation procedures are more suitable for beginners)

说明：  Matlab 实现 一维 PCA 压缩解压 图像，可以设置 eigen value 数目，观看压缩结果。(Matlab to achieve one-dimensional PCA-extracting compressed images, the number of eigen value can be set to watch the results of compression.)

改进型自适应陷波谐波检测法的研究，提高陷波器滤波功能(Improved Adaptive Notch harmonic detection method)

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. )

基于PCNN的特征提取,PCNN用于特征提取时，具体平移、旋转、尺度、扭曲等不变性，这正是许多年来基于内容的图像检索系统追求的目标，同时PCNN用于特征提取时，有很好的抗噪性。而且PCNN直接来自于哺乳动物视觉皮层神经的研究，具有提取图像形状，纹理，边缘的属性。用PCNN能很好地对图像进行签名，将二维的图像的特征提取成一维矢量签名。(Feature extraction of specified object is an important preprocessing stage in machine vision systems. In this paper, we present a novel hybrid feature extraction method using PCNN (Pulse Coupled Neural Network) and shape information. First, we use PCNN firing map train to formulate object’s time signature, then we use roundness of each firing map to formulate object’s shape information vector, the final feature matrix we got is combined time signature and roundness. We take correlations as our judge criteria in our experiments. It has been proved that the algorithm is not sensitivity with the rotation, scaling and translation of the object and is a useful method for target recognition applications.)

超分辨率中要使用的分块重叠及恢复，任意像素重合，超简单！(Super-resolution block overlap and recovery, to use in any pixel overlap, super easy!)