关键词: 超分辨率, 聚类, 核主成分分析, 距离约束

Abstract:

The match between the observed example and the training example set is one of the crucial problem in learning based super resolution. The proposed method can make the match more accurate by mapping the observation example of low resolution to the reproducing kernel Hilbert space, avoiding the wrong match in the learning based super resolution and improving the image guality. The algorithm is that first to apply KPCA to training examples to form a subspace, and then  project the observed example onto the subspace. The pre-images in input space are obtained using distance constraint algorithm. Finally, the high resolution image is obtained via the recombination of the produced image patches, Experimental results on USPS data set show this method is effective.

Key words: super resolution, clustering, kernel principal components analysis, distance constraint