Image Restoration with DnCNN and NAFNet

This project explores deep learning models for restoring blurry low-resolution images. It compares a custom DnCNN-based super-resolution model with a transformer-style NAFNet architecture. Both were implemented and evaluated on the GOPRO and RealBlur-R datasets using Colab Pro (A100 GPU).

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🚀 Highlights

• DnCNN-SR: Residual CNN + PixelShuffle-based upsampling
• NAFNet: Transformer-inspired architecture (implemented, not used for demo)
• Metrics: PSNR, SSIM, LPIPS
• Losses: MSE, perceptual (VGG), LPIPS
• Training: AMP, early stopping, ReduceLROnPlateau
• Output visualization and metric summary

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✅ Try It Yourself

You can quickly run the pretrained DnCNN model using:

demo.ipynb

No training needed — just load the weights and run on your own images.

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📊 Performance Comparison

Model	PSNR (↑)	SSIM (↑)	LPIPS (↓)
DnCNN (demo)	26.80	0.8020	0.2313
NAFNet (implemented)	26.73	0.8002	0.2377
Joint model	24.63	0.8670	N/A

DnCNN showed the best perceptual and numerical performance. NAFNet was successfully implemented but not used in the final visualization due to training instability.

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🖼️ Visual Output

Left: LR input (padded, 360x640) | Center: 2x SR output (DnCNN, 720x1280) | Right: HR ground truth
PSNR: 26.80 | SSIM: 0.8020 | LPIPS: 0.2313

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💡 Reflection: Why Simple Beats Complex

We originally tried this cascade:

DnCNN → UNet → EDSR

While promising in theory, this chain:

• Suffered from compounding artifacts
• Was harder to converge
• Did not outperform DnCNN alone in PSNR/SSIM/LPIPS

📌 Conclusion: well-designed single models + quality upsampling outperform deep cascades in image restoration.

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📁 Project Structure

image-restoration/
├── old_joint_model_code/   # Original full pipeline code archive
├── results/                # Output samples + originals + result visualizations + metrics_results
├── LICENSE
├── README.md               # You're reading it
├── demo.ipynb              # Run DnCNN on test images (quick start)
├── dncnn_sr.ipynb          # Full DnCNN model training + results
├── nafnet.ipynb            # Full NAFNet implementation + training (optional)
└── requirements.txt

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📄 Datasets

• GOPRO_Large Dataset
• RealBlur-R Dataset

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🔗 DnCNN Model Weights

You can download pretrained DnCNN weights here: Google Drive

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🛠️ Setup

pip install -r requirements.txt

Then launch demo.ipynb to run DnCNN on your own input images.

This repo includes a pretrained model and demo script. You do not need to train anything to test results.

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🔧 Requirements

• torch
• torchvision
• lpips
• tqdm
• matplotlib
• scikit-image
• opencv-python

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📬 Contact

Maintained by Hank Song For questions, feel free to open an issue or reach out.