Research map / Image generation / Super-resolution
GCFSR: A Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GCFSR instead of GAN-prior-based alternatives | Face SR · CelebA-HQ 16×, 32×, and 64× SR | PSNR: 27.07 -> 27.17; 24.81 -> 24.95; 22.26 -> 22.39 | Helped |
| Use GCFSR instead of GAN-prior-based alternatives | Face SR · CelebA-HQ 16× and 32× SR | FID: 29.99 -> 30.48; 46.00 -> 43.34 | Mixed |
| Train GCFSR with adversarial loss only, without pixel-wise or perceptual loss | GCFSR · CelebA-HQ 4× and 8× SR | LPIPS: 0.1409 -> 0.1210; 0.1736 -> 0.1704 | Helped |
| Add style modulation to generate realistic face details (ablation) | GCFSR · CelebA-HQ 16×, 32×, and 64× SR | perceptual quality | Helped |
| Add L1 and perceptual losses to adversarial training | GCFSR · Large-factor face SR (16×, 32×, 64×) | SR performance | Helped |
Closest papers by meaning
- FSRNet: End-to-End Learning Face Super-Resolution With Facial Priors (CVPR 2018 · similarity 0.89)
- Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes (CVPR 2018 · similarity 0.89)
- GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution (CVPR 2021 · similarity 0.88)
- GSDD: Generative Space Dataset Distillation for Image Super-resolution (AAAI 2024 · similarity 0.87)
- Low-Light Face Super-resolution via Illumination, Structure, and Texture Associated Representation (AAAI 2024 · similarity 0.86)
- Noise-Started One-Step Image Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement (NeurIPS 2026 · similarity 0.85)
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