Abstract: Research on face super-resolution based on deep learning has made significant progress in recent years. However, a challenging aspect in this field is how to effectively restrict model complexity while preserving fine and natural facial textural details during the restoration process, and it’s crucial to meet the demand of transferring the network model onto lightweight devices. Therefore, a lightweight face super-resolution reconstruction method based on pulse attention mechanism is proposed. The proposed new pulse attention mechanism integrates the multi-round global information extracted by the pulse-coupled neural network into the window self-attention mechanism, uses global information and local information to improve the learning ability of the network, and uses the adversarial generation network structure to build a progressive generator based on window self-attention to ensure the lightweighting of the method. Experimental results on the CelebA and Helen datasets show that this method performs excellently on LPIPS and MPS perceptual evaluation indicators. Compared with methods of the same parameter magnitude, it achieves significant improvement across all metrics and exhibits superior subjective visual quality.

Key words: face super-resolution, pulse-coupled neural network, attention mechanism, lightweight network, generative adversarial network