Gradient-vector-diffusion-denoising

用PHD滤波的方法实现多目标跟踪，及对PHD滤波性能的检测。(PHD filter method using multi-target tracking, and performance testing for the PHD filter.)

说明：  在MFC中显示各种格式的图片，jpg.png(Display pictures in various formats in MFC, jpg.png)

陷波滤波器，可调节陷波频域位置，可调节陷波大小，函数，可直接调用，由高斯，理想，巴特沃斯组成。(notchfilter, which is suitable for digital image processing in frequency domain. Notch filter, adjustable notch frequency domain position, adjustable notch size, function, can be called directly, composed of Gauss, ideal, Butterworth.)

提出了一种结合USFFT Curvelet 变换的各向异性扩散图像去噪模型。它有机结合了Curvelet 变换和各向 异性扩散（P-M扩散）两者的优点。通过P-范数方法选择合适的梯度阈值K，P-M扩散过程通过处理经过Curvelet 变换得到的图像的不同尺度的Curvelet 系数矩阵，实现了建立在对图像多尺度分析的基础上的新P-M扩散模型。 实验表明，新模型的处理结果能有效避免传统P-M 扩散出现的阶梯效应，同时更好地保留图像的纹理和细节。 关键词：图像去噪算法；各向异性扩散；Curvelet 变换；P-M 扩散(Abstract: An image de-noising model by integrating anisotropic diffusion with USFFT Curvelet transform was proposed, which combined the strongpoint of Curvelet transform with anisotropic diffusion (P-M diffusion). By choosing appropriate gradient threshold K through p-norms and carrying out the P-M diffusion process depend on the different scale matrixes of Curvelet coefficient of the image from Curvelet transform iterations, as a result, the improved model made it possible to carry out the new P-M diffusion de-noising process based on multi-scale analysis of the image. The experiment results have demonstrated that the model can avert the stair-casing effect in the traditional P-M diffusion effectively and keep the textures and details of images better.)

说明：  经典图像处理灰度图像去噪模型，以及改进模型效果不错(Classic image processing gray image denoising model, and improved model good results.)

此代码为显著性检测代码，显著性检测效果不错(This code is a significant detection code to detect significant good results)

火灾探测中火焰图像分割方法研究,比较实用。(Fire detection fire image segmentation, more practical.)

ViBe算法是由Olivier Barnich 和 Marc Van Droogenbroeck在2011年提出的一种背景建模方法。该算法采用邻域像素来创建背景模型，通过比对背景模型和当前输入像素值来检测前景.(ViBe algorithm is a background modeling method proposed by Olivier Barnich and Marc Van Droogenbroeck in 2011. The neighborhood pixels are used to create the background model, and the foreground is detected by comparing the background model and the current input pixel values)

SPCA The SPCA algorithm for computing sparse principal components.

内容包括高斯混合概率假设密度滤波器仿真程序，GM-PHD滤波器提供了一种PHD滤波器工程化的方法，在无数据关联的情况下进行目标跟踪。(This algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented Kalman filters.)