Adaptive optimal multi-features learning based representation for face hallucination. (15th March 2022)

• Record Type: Journal Article
• Authors: Nagar, Surendra; Jain, Ankush; Singh, Pramod Kumar; Kumar, Ajay
• Is Part Of: Expert systems with applications. Volume 190(2022)
• Publication Date: 2022-03-15
• Subjects: Face hallucination -- Multiple-image-features -- GWO -- Optimization -- Gaussian noise -- Thresholding; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.116141
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: 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. Moreover, an appropriate threshold is applied on different feature samples in the training data for representing the LR patches with their nearest examples. The optimal proportion of these relevant features helps to reconstruct the high-quality HR faces for both noise-free and noisy LR faces. The performance of OMFPL is validated on widely used public databases, real-world images, and surveillance faces, where it achieves the superior results concerning the several competitive state-of-the-art FH methods. Highlights: Multiple image features are obtained for an input image and the LR training samples. The optimal proportion of each image feature is obtained through the GWO. The representation of the LR image features is done by the nearest training features.