# Learnable Telegraph Diffusion for Image Denoising

This repository contains stage-wise trained denoising models for additive Gaussian noise removal, together with a TNRD-style baseline and a classical PDE baseline.

The results below summarize the latest complete overnight sweep:

- log directory: `logs/overnight_20260408_013608`
- evaluated datasets: `Set12`, `BSD68`
- noise levels: `sigma = 15, 25, 50, 75`
- stage count: `5`

Note: the earlier run in `logs/overnight_20260408_013234` was a scheduler-bug run and is not used for the summary below.

## Main Takeaways

- The strongest model in this sweep is the `Finetuned TNRD baseline` on both `Set12` and `BSD68` at every tested noise level.
- For both MLP and RBF parameterizations, the `No-wave` variant outperformed the `Telegraph` variant throughout this sweep.
- The `RBF` parameterization consistently outperformed the corresponding `MLP` parameterization.
- End-to-end fine-tuning improved every model family over its stage-wise checkpoint.
- All learned models clearly outperformed the classical PDE baseline at the tested noise levels.

## Best Results

| Dataset | Sigma | Best model | PSNR (dB) |
|---|---:|---|---:|
| BSD68 | 15 | Finetuned TNRD baseline | 30.90 |
| BSD68 | 25 | Finetuned TNRD baseline | 28.36 |
| BSD68 | 50 | Finetuned TNRD baseline | 25.43 |
| BSD68 | 75 | Finetuned TNRD baseline | 23.91 |
| Set12 | 15 | Finetuned TNRD baseline | 31.85 |
| Set12 | 25 | Finetuned TNRD baseline | 29.33 |
| Set12 | 50 | Finetuned TNRD baseline | 26.05 |
| Set12 | 75 | Finetuned TNRD baseline | 24.18 |

## Plots

### Base Models

![Base model sweep](plots/base_model_sweep.png)

### Finetuned Models

![Finetuned model sweep](plots/finetuned_model_sweep.png)

## BSD68 Results

### Base Models

| Method | 15 | 25 | 50 | 75 |
|---|---:|---:|---:|---:|
| MLP Telegraph | 28.31 | 25.06 | 22.86 | 20.88 |
| MLP No-wave | 29.08 | 26.87 | 23.87 | 21.71 |
| RBF Telegraph | 27.97 | 25.52 | 22.70 | 20.89 |
| RBF No-wave | 30.46 | 27.86 | 24.47 | 22.30 |
| TNRD baseline | 30.41 | 27.85 | 24.58 | 22.42 |

### Finetuned Models

| Method | 15 | 25 | 50 | 75 |
|---|---:|---:|---:|---:|
| Finetuned MLP Telegraph | 29.90 | 27.30 | 24.42 | 22.40 |
| Finetuned MLP No-wave | 29.88 | 27.61 | 24.60 | 22.93 |
| Finetuned RBF Telegraph | 30.56 | 27.70 | 24.65 | 23.26 |
| Finetuned RBF No-wave | 30.79 | 28.30 | 25.23 | 23.74 |
| Finetuned TNRD baseline | 30.90 | 28.36 | 25.43 | 23.91 |

## Set12 Results

### Base Models

| Method | 15 | 25 | 50 | 75 |
|---|---:|---:|---:|---:|
| MLP Telegraph | 29.19 | 25.92 | 23.32 | 20.99 |
| MLP No-wave | 29.59 | 27.47 | 24.54 | 22.28 |
| RBF Telegraph | 29.19 | 26.50 | 23.32 | 21.36 |
| RBF No-wave | 31.45 | 28.97 | 25.43 | 23.12 |
| TNRD baseline | 31.43 | 28.94 | 25.54 | 23.20 |

### Finetuned Models

| Method | 15 | 25 | 50 | 75 |
|---|---:|---:|---:|---:|
| Finetuned MLP Telegraph | 30.63 | 28.03 | 24.76 | 22.38 |
| Finetuned MLP No-wave | 30.64 | 28.40 | 25.10 | 23.12 |
| Finetuned RBF Telegraph | 31.51 | 28.66 | 25.23 | 23.51 |
| Finetuned RBF No-wave | 31.74 | 29.23 | 25.92 | 24.05 |
| Finetuned TNRD baseline | 31.85 | 29.33 | 26.05 | 24.18 |

## Classical PDE Baseline

The latest overnight run evaluated the classical PDE baseline at `sigma = 15, 50, 75`.

| Dataset | 15 | 25 | 50 | 75 |
|---|---:|---:|---:|---:|
| BSD68 | 26.00 | - | 19.56 | 15.08 |
| Set12 | 26.73 | - | 19.55 | 14.98 |

## Notes

- The learned models were trained stage-wise first, then optionally fine-tuned end-to-end on the same noise level.
- Fine-tuned checkpoints and base checkpoints were both evaluated using the same sigma-specific setup.
- `evaluate_checkpoints.py` produced the main result table used here.
- `plot_experiment_results.py` generated the plots in `plots/`.