Superpixel-guided locality quaternion representation for color face hallucination

Recently, the learning based methods have been well exploited to hallucinate grayscale face images. When facing color images, however, the previous approaches either suffering from the non-flexibility for arbitrary pattern shapes or ignoring the inherent color information. To address these concerns, in this paper we propose a new learning model named as Superpixel-guided Locality Quaternion Representation (SLQR) for color face hallucination. Rather than handling squared patches with fixed size, the proposed method handles superpixels with adaptive shapes segmented from face images according to semantic contents, which can well preserve the face spatial features. Moreover, the superpixels are mapped into quaternion space to exploit the inherent spectral information for color image reconstruction. In addition, considering that images are inevitably corrupted by noise in practice, we extend the SLQR to the robust version (W-SLQR) by introducing a proper reweighting strategy into the objective function to suppress noise. Compared to some state-of-the-art methods, various experiments have been conducted to verify the superiority of our proposed methods in hallucinating clean and noisy face images.