Noise Robust Face Hallucination via Outlier Regularized Least Square and Neighbor Representation

摘要

In surveillance scenario the captured face may be of small size, low-quality, low-resolution and noisy. Noise introduces outliers in the captured face images which cause problems in similarity matching, an essential component in attaining the face reconstruction constraints. The situation becomes even more complicated when face images are corrupted by mixed Gaussian-Impulse noise (MIXGIN). To address this problem, a novel outlier regularized least square and neighbor representation (ORLSNR) based face hallucination method is proposed here. The proposed method starts with the detection of the outliers in an input face and performs outlier regularization to reduce the impact of outliers on the reconstruction produce. This assists in achieving the sparsity and locality simultaneously by allowing the selection of the most relevant patches for reconstruction of the high-resolution face. Experimental results performed on public FEI, CMU+MIT face databases, and surveillance videos reflect that the proposed method is computationally efficient and demonstrate better performance than the existing state-of-the-art face hallucination methods.