Modeling and Optimizing of the Multi-Layer Nearest Neighbor Network for Face Image Super-Resolution

Abstract

In this paper, we propose a face super-resolution (FSR) method to handle the decreasing face recognition rate caused by low-quality images. To better model the input images, we build a nearest neighbor network (NNN) which consists of nodes and paths by introducing the second-layer nearest neighbors (SLNNs), where the paths of the network represent the distance between nodes. As the SLNN is trained in the high-resolution (HR) space and is exponentially supplementary to the traditional first-layer nearest neighbors (FLNNs), the neighbor inadequacy problem can be effectively solved by enriching the neighbor candidate set via NNN. Furthermore, we solve the NNN for the optimal weights of neighbors. Finally, we fuse the refined weights and neighbors for better reconstruction results. The effectiveness of this fusion strategy is validated by both quantitative and qualitative experimental results. The extensive experimental results on the public face datasets and real-world challenging low-resolution (LR) images demonstrate that the proposed method performs favorably against the state-of-the-art methods.