Adaptive optimal multi-features learning based representation for face hallucination

摘要: Face hallucination (FH) is a classical problem to reconstruct a high-resolution (HR) face image for an observed low-resolution (LR) one. The existing methods represent LR facial images though the spatial pixel domain or by combining confined image features with this spatial pixel information. However, the uncertainty in stipulating the optimal proportion for such multiple image features may lead to unexpected results as the optimal proportion for each LR input face image may vary for obtaining the desired HR result. Additionally, they suffer from degraded performance when the observed LR images are contaminated with higher noise. For addressing such problems, this paper proposes an adaptive optimal multi-features proportion learning (OMFPL) scheme, which adopts the Grey Wolf Optimization (GWO) approach for determining the optimum proportion of each feature to represent a particular LR face image …