# Improved Face Image Super-Resolution Model Based on Generative Adversarial Network

## TL;DR

This paper introduces an improved GAN-based model for face image super-resolution that enhances facial details and reduces blurring.

## Contribution

The novel contributions include a Multi-scale Hybrid Attention Residual Block and an Edge-guided Enhancement Block for better facial detail restoration.

## Key findings

- The proposed model achieves a PSNR of 23.35 dB on the CelebA-HQ dataset.
- It outperforms the SwinIR model in PSNR, SSIM, and LPIPS metrics.
- The model delivers superior visual quality in high-frequency facial regions.

## Abstract

Image super-resolution (SR) models based on the generative adversarial network (GAN) face challenges such as unnatural facial detail restoration and local blurring. This paper proposes an improved GAN-based model to address these issues. First, a Multi-scale Hybrid Attention Residual Block (MHARB) is designed, which dynamically enhances feature representation in critical face regions through dual-branch convolution and channel-spatial attention. Second, an Edge-guided Enhancement Block (EEB) is introduced, generating adaptive detail residuals by combining edge masks and channel attention to accurately recover high-frequency textures. Furthermore, a multi-scale discriminator with a weighted sub-discriminator loss is developed to balance global structural and local detail generation quality. Additionally, a phase-wise training strategy with dynamic adjustment of learning rate (Lr) and loss function weights is implemented to improve the realism of super-resolved face images. Experiments on the CelebA-HQ dataset demonstrate that the proposed model achieves a PSNR of 23.35 dB, a SSIM of 0.7424, and a LPIPS of 24.86, outperforming classical models and delivering superior visual quality in high-frequency regions. Notably, this model also surpasses the SwinIR model (PSNR: 23.28 dB → 23.35 dB, SSIM: 0.7340 → 0.7424, and LPIPS: 30.48 → 24.86), validating the effectiveness of the improved model and the training strategy in preserving facial details.