Research map / Architectures and efficiency / CNN design
Fast and Accurate Single Image Super-Resolution via Information Distillation Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use IDN instead of MemNet | Single-image super-resolution · Set5 ×2 | PSNR: 37.78 -> 37.83 | Helped |
| Use IDN instead of MemNet | Single-image super-resolution · Set14 ×3 | PSNR: 30.00 -> 29.99 | Hurt |
| Use IDN instead of MemNet | Single-image super-resolution · BSD100 ×2 | PSNR: 32.08 -> 32.08 | No clear effect |
| Use IDN instead of MemNet | Single-image super-resolution · Urban100 ×3 | PSNR: 27.56 -> 27.42 | Hurt |
| Use IDN instead of MemNet | Single-image super-resolution · Urban100 ×2 | time (sec): 35.871 -> 0.062 | Helped |
Closest papers by meaning
- Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (CVPR 2016 · similarity 0.90)
- Accurate Image Super-Resolution Using Very Deep Convolutional Networks (CVPR 2016 · similarity 0.89)
- Post-trained Convolution Networks for Single Image Super-resolution (Abstract Reprint) (AAAI 2024 · similarity 0.89)
- Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-Resolution (AAAI 2025 · similarity 0.89)
- ResDiff: Combining CNN and Diffusion Model for Image Super-resolution (AAAI 2024 · similarity 0.86)
- Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary (CVPR 2024 · similarity 0.86)
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