基于自学习邻居嵌入的超分辨率特征选择

摘要

给出一些特征提取方法,来提取低分辨率(LR)空间中的块特征,并测试它们选择高分辨率(HR)块的能力.针对邻域嵌入(NE)与训练集的高度相关性,采用自学习方法来生成训练集.部分LR特征可以获得更高质量的HR块,这些HR块与理想的HR块非常相似.自学习实验结果表明,利用所提取的特征可以得到不同的HR重建结果.部分特征提取方法效果好、效率高,所获特征可为基于NE的超分辨率(SR)算法所用,并能解决一对多的不适定问题.%Several feature extraction methods are proposed to extract patch features in low-resolution (LR) space,and their abilities in selecting high-resolution (HR) patches are tested.As neighbor embedding (NE) is highly related to the training sets,the self-learning method is employed to produce the training sets.It can be found that some LR features can obtain higher-quality HR patches which are very similar with the desired HR patches.Experimental results on self-learning show that the proposed methods provide different HR results,some of them have good effect and high efficiency.The obtained features can be used for NE based super resolution (SR) algorithm and can well remit one-to-many ill-posed problems.