The aim of single image super-resolution is to recover a high-resolution image from a single low-resolution image.
This repository includes three implementations of the Super Resolution Convolutional Neural Network (SRCNN) by Dong, Chao, et al. "Image super-resolution using deep convolutional networks." (https://arxiv.org/abs/1501.00092v3)
- 9-5-5 Convolutional Neural Network
- Three implementations
- Torch: For training the network, extracting weights and upscaling
- Numpy: For upscaling
- Cython: For upscaling (able to be run on Avnet's ZedBoard)
- Purely CPU-run or
- Running the 2D convolutions on the ZedBoard's FPGA
See the README.md files of the respective implementation in their folders for usage instructions and prerequisites:
Average of each metric for the Set5 dataset.
| Metric | Bicubic | SCA | NE+LLEA | KKA | ANRA | A+A | SRCNN (Dong) | SRCNN (Ours) |
|---|---|---|---|---|---|---|---|---|
| PSNR | 29.56 | 31.42 | 31.84 | 32.28 | 31.92 | 32.59 | 32.75 | 31.92 |
| SSIM | 0.871 | 0.882 | 0.896 | 0.903 | 0.897 | 0.909 | 0.909 | 0.913 |
| IFC | 3.49 | 3.16 | 4.40 | 4.14 | 4.52 | 4.84 | 4.58 | 4.41 |
| NQM | 27.93 | 27.29 | 32.77 | 32.10 | 33.10 | 34.48 | 33.21 | 33.04 |
| MSSSIM | 0.975 | 0.980 | 0.984 | 0.985 | 0.984 | 0.987 | 0.987 | 0.987 |
- Ground truth is the original (ideal) image for comparison with the upscaled versions.
- Bicubic is the ground truth downscaled by a factor of 3 and upscaled by a factor of 3 using simple bicubic interpolation.
- SRCNN is the same as Bicubic but using the trained convolutional neural network.
| Ground Truth | Bicubic x3 | SRCNN x3 |
|---|---|---|
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