为解决现有单图像超分辨率算法对不同类型图像鲁棒性不强的问题,提出一种基于多模糊核约束的随机森林算法.结合多模糊核扩展随机森林训练阶段的输入特征向量,由低分辨率图像块和对应的模糊核组合生成输入特征变量;将多模糊核引入到决策树构建的质量测度函数中,用于约束决策树构建时的结点划分,使生成的叶结点更纯;采用多模糊核对叶结点的回归模型进行约束,降低决策树的预测误差.仿真结果表明,与主流的基于学习的单图像超分辨率算法相比,该算法对不同图像的鲁棒性更强,采用该方法重建的超分辨率图像的峰值信噪比更高.%For solving the problem that the single image super-resolution algorithm is not robust to different images,a random forest algorithm based on multi-blur kernels constrained was proposed.The input feature vectors for training process of random forest were extended by combining multi-blur kernels,and input feature vectors combined by low resolution image patches and corresponded blur kernels were generated.The multi-blur kernels were introduced into the quality measurement function for building decision tree,to constrain the split nodes while building decision tree and to make the generated leaf nodes more pure.Multi-blur kernels were used to constrain the regression model of leaf nodes to reduce prediction error of the decision tree.Experimental results show that compared with the existing learning-based single image super-resolution algorithms,the proposed method is more robust to different images,and the peak signal to noise ratio of the generated super-resolution images using the proposed method is higher.
展开▼
