Mixed-noise robust face super-resolution through residual-learning based error suppressed nearest neighbor representation

Abstract

The conventional face super-resolution (SR) methods are highly sensitive to noise because of signal conversion error, bit error in transmission, heat, and illumination effects in the camera sensor. This paper proposes a new position-patch based face SR method, namely RLENR, which effectively enhances the low-resolution (LR) facial images impaired with heavy mixed (Gaussian and impulse) noise. Firstly, it suppresses the impulse noise (IN) from the LR face image by utilizing the PCA oriented mate face and the matrix having pixel-wise noise details. Subsequently, it minimizes the effect of Gaussian noise (GN) by incorporating the residual-learning to update the LR training set. Meanwhile, it hallucinates the respective LR face using the updated LR training set. Here, the residual means reconstruction error occurred by the GN. It employs the sparse set of similar patches from the training samples to generate the optimal weights for LR patch representation. Thus, the method simultaneously achieves both the sparsity and locality to recover vital information of the face. The results of extensive experiments on standard facial image dataset, real-world face image dataset, and surveillance face images confirm that the RLENR method quantitatively and visually outperforms state-of-the-art face SR methods.