Miscellaneous Components
PixelShuffle
Introduced by Shi et al. in Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural NetworkPixelShuffle is an operation used in super-resolution models to implement efficient sub-pixel convolutions with a stride of $1/r$. Specifically it rearranges elements in a tensor of shape $(*, C \times r^2, H, W)$ to a tensor of shape $(*, C, H \times r, W \times r)$.
Image Source: Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement
Source: Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural NetworkPapers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Super-Resolution | 30 | 36.59% |
| Image Super-Resolution | 16 | 19.51% |
| Video Super-Resolution | 7 | 8.54% |
| Quantization | 2 | 2.44% |
| Video Restoration | 2 | 2.44% |
| Video Enhancement | 2 | 2.44% |
| License Plate Recognition | 1 | 1.22% |
| Colorization | 1 | 1.22% |
| Key-Frame-based Video Super-Resolution (K = 15) | 1 | 1.22% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
