personal_math

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

Plots

Base Models

Finetuned Models

BSD68 Results

Base Models

Finetuned Models

Set12 Results

Base Models

Finetuned Models

Classical PDE Baseline

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

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/.