Face image super-resolution via weighted patches regression
Yiping Zhang, Zhihong Zhang, Guosheng Hu, Edwin R. Hancock · 2016
Recently sparse representation has gained great success in face image super-resolution. The conventional sparsity-based methods enforce sparse coding on face image patches and the representation fidelity is measured by ℓ2-norm. Such a sparse coding model regularizes all facial patches equally, which however ignores the natures of facial patches, where the facial patches in the different regions (patch positions) of human face may have distinct contributions to face image reconstruction. In this paper, we propose to weight facial patches based on their discriminative abilities in regression for robust face hallucination reconstruction. Specifically, we learn the weights for facial patches according to the information entropy in each face region, so as to highlight higher frequency details in face images and the facial discriminability can be well retrieved. Furthermore, the weighted sparse coding can reasonable represent the less sparse nature of noisy images and thus remarkably boosts noise robust performance in face image super-resolution. Various experimental results on standard face databased show that our proposed method outperforms state-of-the-art methods in terms of both objective metrics and visual quality.