拉普拉斯锐化（边缘检测）

说明：  关于图像处理里的一些应用，将视觉相应算法应用到工程实际中，详细解说了算法在这里面的一些应用，很实用。(On some applications in image processing, the visual corresponding algorithm is applied to the engineering practice, a detailed explanation here below in some applications, the proposed algorithm is very practical.)

活动目标检测源码,用混合高斯算法编写，适用于复杂场景视频监控(alive target detection，used in GMM algorithm)

对图像进行二维离散小波变换, 变换级数大于等于3级,然后进行阈值化处理(阈值约为10左右),再统计系数中0的个数（百分比表示）并进行重构, 最后计算重构图像的峰值信噪比(PSNR). (Two-dimensional discrete wavelet transform image transformed series is greater than or equal to 3, and then processing thresholding (threshold of about 10 or so), then statistical coefficient in the number (expressed as a percentage) and to reconstruct the final calculation reconstructed image of the peak signal-to-noise ratio (PSNR).)

说明：  通过双通道PCNN来实现多源图像融合，算法简单易于实现(image fusion using dual channel PCNN)

包含二维曲波变换、反变换；三维曲波变换；二维曲波变换(Including two-dimensional Qu Bo transform, inverse transform, 3D Qu Bo transform, and two-dimensional Qu Bo transform)

sift算法，特征点的提取与匹配，图像融合，图像拼接。测试过，效果不错，特征点提取较稳定。(Sift u7B97 u6CD2 uFF0C u7279 u5F1 u70B9 u7684 u63D0 u53D6 u4E0E u5339 u914D uFF0C u56FE u50CF u878D u5408 uFF0C u56FE u50CF u62FC u63A5 u3002 u6D4B U8BD5 u8FC3 uFF0C u6548 u679C u4E0D u9519 uFF0C u7279 u5F81 u70B9 u63D0 u53D6 u8F83 u7A33 u5B9A u3002)

主要有图像的聚类分割和Ncut算法的彩色图像分割，对于新人有着很好的效果(There are clustering image segmentation algorithm and Ncut color image segmentation, it has a good effect for newcomers)

说明：  基于双边滤波的retinex算法，暗光图像增强，有效去噪、提高对比度、去光晕；含论文和讲解PPT、实例代码(Retinex algorithm based on bilateral filtering can enhance dark image, effectively denoise, improve contrast and eliminate halo)

图像反卷积 来自D. Krishnan, R. Fergus中提到的方法(image deconvolution The Matlab functions in this directory solve the deconvolution problem in the paper D. Krishnan, R. Fergus: "Fast Image Deconvolution using Hyper-Laplacian Priors", Proceedings of NIPS 2009. )

斑点噪声的去除方法，主要适用sar图像，比较适合开题使用(Speckle noise removal methods, mainly for sar image, more suitable for the use of open questions)