GFNet: A Gradient Information Compensation-Based Face Super-Resolution Network

Abstract

Face super-resolution (FSR) is defined as the generation of high-resolution face images from low-resolution face images. Existing FSR approaches usually improve the performance by combining deep learning with additional tasks such as face parsing and landmark prediction. However, the additional data requires manual labeling, and facial landmark heatmaps and parsing maps cannot represent the intrinsic geometric structure of facial components. In this paper, we introduce a FSR network based on gradient information compensation named GFNet, which consists of feature residual blocks (FRBs) and gradient extraction blocks (GEBs). Specifically, the GEB constructs pixel-level gradient maps directly from the feature maps without requiring data labels and extracts gradient features to compensate for the missing high-frequency components in the face features; the FRB extracts the face features in the network. Furthermore, we introduced a feature fusion mechanism between the GEB and the FRB, which fuses the face features with the gradient features. We evaluate the performance of proposed network on the two public datasets: CelebA-HQ dataset and Helen dataset. Experimental results show that the proposed method is able to reconstruct fine face images, which outperforms the other state-of-the-art methods such as SRResnet, FSRNet, and MSFSR.

Keywords

• Super-resolution
• face hallucination
• gradient information
• feature fusion