Abstract
A novel face hallucination method is proposed in this paper for the reconstruction of a high-resolution face image from Sparse Representation. By joint training two dictionaries for the low-and high-resolution image patches, the method efficiently builds sparse association between high-frequency components of HR image patches and LR image feature patches, and defines the association as a prior knowledge, Using MAP criteria to guide super-resolution reconstruction with respect to their own dictionaries. The learned Dictionary pair is a more compact representation of the patch pairs, reducing the computational cost substantially. Experiments show that the proposed method generates higher-quality images and costs less computational time than some recent face image super-resolution (hallucination) techniques, and achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.
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