Research map / Image generation / Super-resolution
Deeply-Recursive Convolutional Network for Image Super-Resolution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DRCN rather than SRCNN | Single-image super-resolution · Set5, Set14, B100, Urban100 at ×2, ×3, and ×4 | PSNR/SSIM | Helped |
| Increase recursion depth (ablation) | DRCN · Set5 ×3 | PSNR | Helped |
| Ensemble intermediate recursion predictions instead of using an individual prediction (ablation) | DRCN · Set5, across scale factors | PSNR | Helped |
| Apply supervision to every recursion and combine the intermediate predictions (ablation) | DRCN · Super-resolution benchmarks | PSNR | Helped |
| Add a skip connection from the input to reconstruction (ablation) | DRCN · Super-resolution benchmarks | PSNR | Helped |
Closest papers by meaning
- Image Super-Resolution via Deep Recursive Residual Network (CVPR 2017 · similarity 0.94)
- Accurate Image Super-Resolution Using Very Deep Convolutional Networks (CVPR 2016 · similarity 0.91)
- Image Super-Resolution via Dual-State Recurrent Networks (CVPR 2018 · similarity 0.89)
- Feedback Network for Image Super-Resolution (CVPR 2019 · similarity 0.88)
- Deep Back-Projection Networks for Super-Resolution (CVPR 2018 · similarity 0.88)
- Residual Dense Network for Image Super-Resolution (CVPR 2018 · similarity 0.88)
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