Eigentransformation-based face super-resolution in the wavelet domain

In this paper, we propose a wavelet-based eigentransformation method for human face hallucination. Our algorithm uses the wavelet transform to decompose interpolated low-resolution (LR) images in the wavelet domain to obtain high-frequency information in three different directions, and employs the eigentransformation method to reconstruct the corresponding finer high-frequency content of the high-resolution (HR) images. The low-frequency content of the HR images in the wavelet domain is estimated based on the interpolated images directly. The resulting high-quality HR faces can be synthesized by using the inverse wavelet transform, with all the estimated coefficients. By combining interpolation and eigentransformation, the reconstructed images are less dependent on the training set selected, and can better preserve the low-frequency content. Thus, the reconstructed images look more like the ground-true HR images, as compared to the original eigentransformation method. Experimental results show that our proposed algorithm outperforms the original eigentransformation and other existing methods for face hallucination in terms of both visual quality and objective measurements.