合稀疏贝叶斯学习(SBL)和可压缩传感理论(CS)，给出一种在噪声测量条件下重建可压缩图像的方法。该方法将cS理论中图像重建过程看作一个线性回归问题，而待重建的图像是该回归模型巾的未知权值参数；利用sBL方法对权值赋予确定的先验条件概率分布用以限制模型的复杂度，并引入超参数- Hop sparse Bayesian learning ( SBL ) and compressible sensing theory ( CS ) , give a compressible image recon

本程序的性能已经达到较高水平，matlab开发工具箱中的支持向量机，IDW距离反比加权方法。- The performance of the program has reached a high level, matlab development toolbox support vector machine, IDW inverse distance weighting method.

采用热核构造权重，结合PCA的尺度不变特征变换(SIFT)算法，对于初学者具有参考意义。- Thermonuclear using weighting factors Combined with PCA scale invariant feature transform (SIFT) algorithm, For beginners with a reference value.

实现了图像的灰度化并进一步用于视频监视控，采用热核构造权重，具有丰富的参数选项。- Achieve a grayscale image and further control for video surveillance, Thermonuclear using weighting factors It has a wealth of parameter options.