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
Finetuned Models
Finetuned model sweep
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/.
