DAFNet: A Dual Attention Fusion-Based Face Super-Resolution Network

Abstract

With the development of convolutional neural networks (CNNs), deep learning-based face super-resolution (FSR) approaches have achieved remarkable results in recent years. However, existing FSR methods often rely on face prior knowledge, which greatly increases network complexity and computation. In this paper, we propose DAFNet, a dual attention fusion-based FSR network comprising numerous face attention fusion modules (FAFMs). FAFM is a residual structure containing attention mechanism, which is divided into feature branches and attention branches. The feature branch introduces an hourglass block to extract multi-scale information, while the attention branch incorporates channel attention and spatial attention in series. This design ensures that the network prioritizes important information and effectively recovers detailed facial features. Luminance-chrominance error loss and gradient loss are introduced to guide the training process. Additionally, adversarial loss and perceptual loss are incorporated to enhance the recovery of visually realistic face images. Notably, our method can produce clear faces at a high-scale factor of eight times without relying on any facial prior information, effectively reducing network complexity. Quantitative and qualitative experiments conducted on the CelebA and Helen datasets underscore the effectiveness of the proposed model.