Alignment-Free and High-Frequency Compensation in Face Hallucination

Abstract EN

Face hallucination is one of learning-based super resolution techniques, which is focused on resolution enhancement of facial images.

Though face hallucination is a powerful and useful technique, some detailed high-frequency components cannot be recovered.

It also needs accurate alignment between training samples.

In this paper, we propose a high-frequency compensation framework based on residual images for face hallucination method in order to improve the reconstruction performance.

The basic idea of proposed framework is to reconstruct or estimate a residual image, which can be used to compensate the high-frequency components of the reconstructed high-resolution image.

Three approaches based on our proposed framework are proposed.

We also propose a patch-based alignment-free face hallucination.

In the patch-based face hallucination, we first segment facial images into overlapping patches and construct training patch pairs.

For an input low-resolution (LR) image, the overlapping patches are also used to obtain the corresponding high-resolution (HR) patches by face hallucination.

The whole HR image can then be reconstructed by combining all of the HR patches.

Experimental results show that the high-resolution images obtained using our proposed approaches can improve the quality of those obtained by conventional face hallucination method even if the training data set is unaligned.