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

Finetuned Models

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/.