OEINR-RFH: Outlier elimination based iterative neighbor representation for robust face hallucination

Abstract

To make the face hallucination process robust to impulse noise, a new outlier elimination based iterative neighbor representation (OEINR) algorithm is proposed in this work. The proposed algorithm computes the fidelity in the low-resolution (LR) space and the locality adaptor in the high-resolution (HR) space. It helps the proposed algorithm in obtaining better reconstruction weights and preserving individual characteristics of an input in the output faces. To compute the HR space-based locality adaptor, an HR face (named the assisting face) is generated by proposing a new objective function utilizing the merits of sparsity. Further, the iterative approach is applied to continuously improve the quality of the assisting face which succors in achieving better locality constraint, and hence better reconstruction weights. Moreover, outliers (impulse noise-contaminated pixels) are eliminated from weights calculation as well as the assisting face generation process which helps the proposed algorithm in minimizing the effect of impulse noise. The results obtained from the experiments conducted on public FEI, CAS+PEAL, CMU+MIT face databases, and surveillance videos and images exhibit the superiority of the proposed OEINR algorithm over existing comparative face hallucination methods.