---
tags:
- image-super-resolution
- image-denoising
- pytorch
---

# Low-Light Denoising + 4x Super-Resolution

Compact RRDB-style CNN trained for the DLP 26T2 NPPE3 Kaggle competition. Takes a noisy,
low-light, low-resolution RGB image and outputs a denoised 4x super-resolved image.

- Params: 6.13M
- Best validation PSNR: 38.86 dB
- Architecture: 8 RRDB blocks, 64 features,
  pixel-shuffle upsampling, trained with L1 loss.

## Usage
```python
import torch
from model import DenoiseSRNet  # architecture defined in this notebook

model = DenoiseSRNet(nf=64, gc=32,
                      n_blocks=8, scale=4)
model.load_state_dict(torch.load("best.pth", map_location="cpu"))
model.eval()
```