基于向量机的图像插值算法研究

为了更好地进行图像插值，可首先利用最小二乘向量机对原始图像的局部区域进行灰度曲面最佳拟合，然后在拟合曲面上进行未知像素点的灰度值估计，同时提出了运用测试图像进行参数优化的方法，并以径向基核函数为例导出了区域图像的插值系数矩阵，进行了图像放大插值实验验证。实验结果表明，基于支持向量机的图像插值算法具有很强的适应性，其性能与Cubic技术相当，但效率更高。

Research of SVM-based Image Interpolation Algorithm Optimization

The image intensity surface for the local neighborhood of image is well fitted by the least squares support vector machine (lssvm), and then the gray level interpolation is implemented on the fitted intensity surface. The interpolation coefficient matrix of the local neighborhood is deduced from the lssvm with the radial basis function (rbf) kernel function, as an example. A method using the interpolation evaluating merit figure, psnr, to optimize the svm parameters is proposed. With the selected parameters, the computer interpolation experiments are carried out. The experimental results demonstrate the svm based interpolation algorithm has similar performance to cubic one but providing higher efficiency.