Three-tiered network model for image hallucination
Lin Ma, Yonghua Zhang, Yan Lü, Feng Wu, Debin Zhao · 2008
In this paper, we propose a novel three-tiered network model for image hallucination based on the learnt knowledge composed of image patches relating low and high resolution. A common problem of previous hallucination methods is that irregularities are usually introduced into the constructed high-resolution images. We remove the irregularities in three steps. First, the hallucination with primal sketch priors is performed to construct a coarse high-frequency component. Second, enhancement is implemented to enforce local compatibility between the patches in the constructed component. Third, a Markov network is utilized to refine the enhanced high-frequency component. Experiments demonstrate that our model can hallucinate higher-quality images than existing methods.