期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 9, 期 4, 页码 888-892出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2007.893346
关键词
face; higher-order statistics; kernel PCA; principal component analysis (PCA); super-resolution
We present a learning-based method to super-resolve face images using a kernel principal component analysis-based prior model. A prior probability is formulated based on the energy lying outside the span of principal components identified in a higher-dimensional feature space. This is used to regularize the reconstruction of the high-resolution image. We demonstrate with experiments that including higher-order correlations results in significant improvements.
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