Research map / Image generation / Image restoration
Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the TDAE decoder-encoder-decoder pipeline instead of the strongest baseline | 8× hallucination of unaligned, noisy 16×16 faces · CelebA test | PSNR: 1.82 dB | Helped |
| Apply multiple spatial transformers to feature maps while upsampling | TDAE · CelebA test, unaligned noisy faces | PSNR | Helped |
| Project intermediate HR faces to aligned, noise-free LR faces before the second decoder | TDAE · CelebA test, unaligned noisy faces | PSNR | Helped |
Closest papers by meaning
- Face Hallucination with Tiny Unaligned Images by Transformative Discriminative Neural Networks (AAAI 2017 · similarity 0.93)
- Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes (CVPR 2018 · similarity 0.91)
- Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination (CVPR 2022 · similarity 0.87)
- GCFSR: A Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors (CVPR 2022 · similarity 0.86)
- Low-Light Face Super-resolution via Illumination, Structure, and Texture Associated Representation (AAAI 2024 · similarity 0.84)
- Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications (ICML 2024 · similarity 0.83)
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