Multi-Scale Feature Fusion and Structure-Preserving Network for Face Super-Resolution

Multi-Scale Feature Fusion and Structure-Preserving Network for Face Super-Resolution

Blog Article

Deep convolutional neural networks have demonstrated significant performance improvements in face super-resolution tasks.However, many deep learning-based approaches tend to overlook the inherent structural information and feature correlation across different scales in face images, making the accurate recovery of face structure in low-resolution cases challenging.To address this, this paper proposes a method that fuses multi-scale features while preserving the facial structure.It introduces a novel multi-scale residual block (MSRB) to reconstruct key facial parts and structures from spatial and channel Generators dimensions, and utilizes pyramid attention (PA) to exploit non-local self-similarity, improving the details of the reconstructed face.Feature Enhancement Modules (FEM) are employed in the upscale stage to refine and enhance current features using multi-scale features from previous stages.

The experimental results on CelebA, Helen and LFW datasets provide evidence that our method achieves superior quantitative metrics compared to the baseline, the Peak Signal-to-Noise Ratio (PSNR) outperforms the baseline by 0.282 dB, 0.343 dB, and 0.336 dB.Furthermore, our method demonstrates improved visual performance on two additional Medium Weight no-reference datasets, Widerface and Webface.