PCNNSR: Facial Image Super-Resolution via Pulse-Coupled Neural Network Attention Mechanism

Transformer-based methods have made impressive progress in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information. Some networks enlarge the receptive field by expanding the window of self-attention leading to suffer from intensive computations. And how to ensure that the fine and natural texture details of the image are recovered while effectively reducing the model complexity to meet the demand of migrating the use on lightweight devices is a difficult problem in this field. In this paper, we present a lightweight facial image super-resolution reconstruction algorithm. It introduces the Pulse-Coupled Neural Network (PCNN) into attention mechanism thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to reduce the computational workload, the overall model adopts an generative adversarial network structure to incrementally generate images. We trained the model on the CelebA dataset and performed comprehensive performance and generalization tests on the remaining 200 images of the CelebA and Helen datasets. Experimental results show that our method achieves competitive performance while maintaining an lower parameter count when compared with previous leading methods, especially obtaining the highest LPIPS and MPS scores. Compared to methods with similar parameter magnitudes, our network shows significant improvements in all metrics, notably, a reduction in the FID by 45.9.