Low-Light Denoising + 4× Super-Resolution (NAFNet)
A joint denoising and 4× super-resolution model for noisy low-light images, mapping 256×160 LR inputs to 1024×640 HR outputs.
Recovered Degradation Model
The image degradation process recovered from the training data is:
LR = round(clip(box4(HR) + N(0, sqrt(2.514 × I + 2.037))))
where box4 denotes 4× box downsampling and the noise follows an intensity-dependent Gaussian model.
Load the Model
from huggingface_hub import hf_hub_download
import torch
ckpt_path = hf_hub_download(
repo_id="piushdasss/nppe-lowlight-sr-nafnet",
filename="model.pt",
)
ckpt = torch.load(
ckpt_path,
map_location="cpu",
)
Results
| Metric | Result |
|---|---|
| Test-like holdout PSNR | 37.131 dB |
| Official validation PSNR | 39.3 dB |
| Model parameters | 2.30M |
Low-Light Denoising + 4× Super-Resolution (NAFNet)
A joint denoising and 4× super-resolution model for noisy low-light images, mapping 256×160 LR inputs to 1024×640 HR outputs.
Recovered Degradation Model
The image degradation process recovered from the training data is:
LR = round(clip(box4(HR) + N(0, sqrt(2.514 × I + 2.037))))
where box4 denotes 4× box downsampling and the noise follows an intensity-dependent Gaussian model.
Load the Model
from huggingface_hub import hf_hub_download
import torch
ckpt_path = hf_hub_download(
repo_id="piushdasss/nppe-lowlight-sr-nafnet",
filename="model.pt",
)
ckpt = torch.load(
ckpt_path,
map_location="cpu",
)
Results
| PSNR Set | Result |
|---|---|
| Holdout | 37.131 dB |
| Validation set | 39.3 dB |
| Model parameters | 2.30M |