Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution

Highlights：

• We focus on face image super resolution scenarios where the testing images have various noises.

• We propose an efficient multilayer locality-constrained matrix regression (MLCMR) framework to learn the representation of the input LR patch and meanwhile preserve the manifold of the original HR space.

• MLCMR uses nuclear norm regularization to capture the structural information of the representation residual and applies an adaptive neighborhood selection scheme to find the HR patches that are compatible with its neighbors.

• Experimental results demonstrate the effectiveness of our method.