Abstract
In this paper, a strategy of reconstructing high resolution facial image based on that of low resolution is put forward. Rather than only relying on low resolution input image, we construct a face representation dictionary based on training high resolution facial images to compensate for the information difference between low and high resolution images. This restoration is realized through enrolling a low resolution facial image dictionary which is acquired through directly downsampling the learned high resolution dictionary. After the representation coefficient vector of a low resolution input image on low resolution dictionary is obtained through e1-optimization algorithm, this coefficient can be transplanted into high resolution dictionary directly to restore the high resolution image corresponding to input face. This approach was validated on the Extended Yale database.
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