SSR2: Sparse signal recovery for single-image super-resolution on faces with extreme low resolutions

Highlights：

• This paper introduces a novel technique to extract a sparse feature vector from extreme low resolution face images. This feature vector enables us to synthesize a high-resolution face image with magnification factors up to 16x from a single input face. We show how our method is robust to noise and handles real-world low-resolution faces.

• The significance of this paper lies in the fact that despite its straightforward mathematical foundations (simple subspace modeling followed by a sparse feature extraction step), it yields reconstruction results that comprehensively exceed state of the art and more convoluted methods (such as deep leaning methods SRCNN and SRGAN). Moreover, our method only requires a single input face to perform super resolution.