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Sync full project: code, checkpoints, datasets, logs

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+ datasets/Test_Datasets/FFDNet-master/testsets/RNI15/Pattern3.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/RNI15/Postcards.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/RNI15/Singer.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/RNI15/Stars.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/RNI15/Window.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/RNI6/Building.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/RNI6/Marilyn.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/Set12/08.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/Set12/09.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/Set12/10.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/Set12/11.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/testsets/Set12/12.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/utilities/figs/102061_75_75.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/utilities/figs/102061_75_75_PSNR_2698.png filter=lfs diff=lfs merge=lfs -text
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+ datasets/Test_Datasets/FFDNet-master/utilities/figs/Frog.gif filter=lfs diff=lfs merge=lfs -text
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+ plots/base_model_sweep.png filter=lfs diff=lfs merge=lfs -text
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+ plots/finetuned_model_sweep.png filter=lfs diff=lfs merge=lfs -text
.gitignore ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Secrets — never commit
2
+ .env
3
+ .env.*
4
+ !.env.example
5
+
6
+ # Python
7
+ __pycache__/
8
+ *.py[cod]
9
+ .Python
10
+ *.egg-info/
11
+ .venv/
12
+ venv/
13
+
14
+ # IDE / local
15
+ .cursor/
16
+ *.swp
17
+
18
+ # OS
19
+ .DS_Store
README.md ADDED
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1
+ # Learnable Telegraph Diffusion for Image Denoising
2
+
3
+ This repository contains stage-wise trained denoising models for additive Gaussian noise removal, together with a TNRD-style baseline and a classical PDE baseline.
4
+
5
+ The results below summarize the latest complete overnight sweep:
6
+
7
+ - log directory: `logs/overnight_20260408_013608`
8
+ - evaluated datasets: `Set12`, `BSD68`
9
+ - noise levels: `sigma = 15, 25, 50, 75`
10
+ - stage count: `5`
11
+
12
+ Note: the earlier run in `logs/overnight_20260408_013234` was a scheduler-bug run and is not used for the summary below.
13
+
14
+ ## Main Takeaways
15
+
16
+ - The strongest model in this sweep is the `Finetuned TNRD baseline` on both `Set12` and `BSD68` at every tested noise level.
17
+ - For both MLP and RBF parameterizations, the `No-wave` variant outperformed the `Telegraph` variant throughout this sweep.
18
+ - The `RBF` parameterization consistently outperformed the corresponding `MLP` parameterization.
19
+ - End-to-end fine-tuning improved every model family over its stage-wise checkpoint.
20
+ - All learned models clearly outperformed the classical PDE baseline at the tested noise levels.
21
+
22
+ ## Best Results
23
+
24
+ | Dataset | Sigma | Best model | PSNR (dB) |
25
+ |---|---:|---|---:|
26
+ | BSD68 | 15 | Finetuned TNRD baseline | 30.90 |
27
+ | BSD68 | 25 | Finetuned TNRD baseline | 28.36 |
28
+ | BSD68 | 50 | Finetuned TNRD baseline | 25.43 |
29
+ | BSD68 | 75 | Finetuned TNRD baseline | 23.91 |
30
+ | Set12 | 15 | Finetuned TNRD baseline | 31.85 |
31
+ | Set12 | 25 | Finetuned TNRD baseline | 29.33 |
32
+ | Set12 | 50 | Finetuned TNRD baseline | 26.05 |
33
+ | Set12 | 75 | Finetuned TNRD baseline | 24.18 |
34
+
35
+ ## Plots
36
+
37
+ ### Base Models
38
+
39
+ ![Base model sweep](plots/base_model_sweep.png)
40
+
41
+ ### Finetuned Models
42
+
43
+ ![Finetuned model sweep](plots/finetuned_model_sweep.png)
44
+
45
+ ## BSD68 Results
46
+
47
+ ### Base Models
48
+
49
+ | Method | 15 | 25 | 50 | 75 |
50
+ |---|---:|---:|---:|---:|
51
+ | MLP Telegraph | 28.31 | 25.06 | 22.86 | 20.88 |
52
+ | MLP No-wave | 29.08 | 26.87 | 23.87 | 21.71 |
53
+ | RBF Telegraph | 27.97 | 25.52 | 22.70 | 20.89 |
54
+ | RBF No-wave | 30.46 | 27.86 | 24.47 | 22.30 |
55
+ | TNRD baseline | 30.41 | 27.85 | 24.58 | 22.42 |
56
+
57
+ ### Finetuned Models
58
+
59
+ | Method | 15 | 25 | 50 | 75 |
60
+ |---|---:|---:|---:|---:|
61
+ | Finetuned MLP Telegraph | 29.90 | 27.30 | 24.42 | 22.40 |
62
+ | Finetuned MLP No-wave | 29.88 | 27.61 | 24.60 | 22.93 |
63
+ | Finetuned RBF Telegraph | 30.56 | 27.70 | 24.65 | 23.26 |
64
+ | Finetuned RBF No-wave | 30.79 | 28.30 | 25.23 | 23.74 |
65
+ | Finetuned TNRD baseline | 30.90 | 28.36 | 25.43 | 23.91 |
66
+
67
+ ## Set12 Results
68
+
69
+ ### Base Models
70
+
71
+ | Method | 15 | 25 | 50 | 75 |
72
+ |---|---:|---:|---:|---:|
73
+ | MLP Telegraph | 29.19 | 25.92 | 23.32 | 20.99 |
74
+ | MLP No-wave | 29.59 | 27.47 | 24.54 | 22.28 |
75
+ | RBF Telegraph | 29.19 | 26.50 | 23.32 | 21.36 |
76
+ | RBF No-wave | 31.45 | 28.97 | 25.43 | 23.12 |
77
+ | TNRD baseline | 31.43 | 28.94 | 25.54 | 23.20 |
78
+
79
+ ### Finetuned Models
80
+
81
+ | Method | 15 | 25 | 50 | 75 |
82
+ |---|---:|---:|---:|---:|
83
+ | Finetuned MLP Telegraph | 30.63 | 28.03 | 24.76 | 22.38 |
84
+ | Finetuned MLP No-wave | 30.64 | 28.40 | 25.10 | 23.12 |
85
+ | Finetuned RBF Telegraph | 31.51 | 28.66 | 25.23 | 23.51 |
86
+ | Finetuned RBF No-wave | 31.74 | 29.23 | 25.92 | 24.05 |
87
+ | Finetuned TNRD baseline | 31.85 | 29.33 | 26.05 | 24.18 |
88
+
89
+ ## Classical PDE Baseline
90
+
91
+ The latest overnight run evaluated the classical PDE baseline at `sigma = 15, 50, 75`.
92
+
93
+ | Dataset | 15 | 25 | 50 | 75 |
94
+ |---|---:|---:|---:|---:|
95
+ | BSD68 | 26.00 | - | 19.56 | 15.08 |
96
+ | Set12 | 26.73 | - | 19.55 | 14.98 |
97
+
98
+ ## Notes
99
+
100
+ - The learned models were trained stage-wise first, then optionally fine-tuned end-to-end on the same noise level.
101
+ - Fine-tuned checkpoints and base checkpoints were both evaluated using the same sigma-specific setup.
102
+ - `evaluate_checkpoints.py` produced the main result table used here.
103
+ - `plot_experiment_results.py` generated the plots in `plots/`.
baseline_robustness_curve.png ADDED

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1
+ import os
2
+ import glob
3
+ import torch
4
+ import torch.nn.functional as F
5
+ import math
6
+ from PIL import Image
7
+ from torchvision import transforms
8
+ from torch.utils.data import Dataset, DataLoader
9
+
10
+ # --- Configuration ---
11
+ DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
12
+ _SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
13
+ _SIGMAS = (15.0 / 255.0, 50.0 / 255.0, 75.0 / 255.0)
14
+
15
+
16
+ def _testset_root(name):
17
+ return os.path.join(
18
+ _SCRIPT_DIR,
19
+ "datasets",
20
+ "Test_Datasets",
21
+ "FFDNet-master",
22
+ "testsets",
23
+ name,
24
+ )
25
+
26
+
27
+ class TestDataset(Dataset):
28
+ def __init__(self, root_dir, sigma):
29
+ self.sigma = sigma
30
+ self.image_paths = glob.glob(os.path.join(root_dir, "*.png")) + glob.glob(
31
+ os.path.join(root_dir, "*.jpg")
32
+ )
33
+ self.transform = transforms.Compose(
34
+ [transforms.Grayscale(num_output_channels=1), transforms.ToTensor()]
35
+ )
36
+
37
+ def __len__(self):
38
+ return len(self.image_paths)
39
+
40
+ def __getitem__(self, idx):
41
+ img = Image.open(self.image_paths[idx])
42
+ clean = self.transform(img)
43
+ noisy = clean + torch.randn_like(clean) * self.sigma
44
+ return clean, torch.clamp(noisy, 0.0, 1.0)
45
+
46
+
47
+ def calculate_psnr(img1, img2):
48
+ mse = torch.mean((img1 - img2) ** 2)
49
+ if mse == 0:
50
+ return float("inf")
51
+ return 20 * math.log10(1.0 / math.sqrt(mse))
52
+
53
+
54
+ def classical_telegraph_step(u_n, u_n_minus_1, tau=0.2, gamma=1.0):
55
+ kx = torch.tensor([[0, 0, 0], [-0.5, 0, 0.5], [0, 0, 0]], device=DEVICE).view(
56
+ 1, 1, 3, 3
57
+ )
58
+ ky = torch.tensor([[0, -0.5, 0], [0, 0, 0], [0, 0.5, 0]], device=DEVICE).view(
59
+ 1, 1, 3, 3
60
+ )
61
+
62
+ grad_x = F.conv2d(u_n, kx, padding=1)
63
+ grad_y = F.conv2d(u_n, ky, padding=1)
64
+ grad_mag = torch.sqrt(grad_x**2 + grad_y**2 + 1e-8)
65
+
66
+ c = 1.0 / (1.0 + (grad_mag / 0.1) ** 2)
67
+
68
+ divergence = F.conv2d(c * grad_x, kx, padding=1) + F.conv2d(
69
+ c * grad_y, ky, padding=1
70
+ )
71
+
72
+ alpha = (2 + gamma * tau) / (1 + gamma * tau)
73
+ beta = -1 / (1 + gamma * tau)
74
+ lam = (tau**2) / (1 + gamma * tau)
75
+
76
+ return alpha * u_n + beta * u_n_minus_1 + lam * divergence
77
+
78
+
79
+ def run_eval(dataset_name, sigma):
80
+ root = _testset_root(dataset_name)
81
+ dataset = TestDataset(root, sigma)
82
+ if len(dataset) == 0:
83
+ print(f"[!] Skip {dataset_name}: no images in {os.path.abspath(root)}")
84
+ return None
85
+
86
+ dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
87
+ total_psnr = 0.0
88
+ for clean, noisy in dataloader:
89
+ clean, noisy = clean.to(DEVICE), noisy.to(DEVICE)
90
+ u_n_minus_1, u_n = noisy.clone(), noisy.clone()
91
+
92
+ for _ in range(20):
93
+ u_next = classical_telegraph_step(u_n, u_n_minus_1)
94
+ u_n_minus_1, u_n = u_n, u_next
95
+
96
+ total_psnr += calculate_psnr(clean, u_n)
97
+
98
+ avg_psnr = total_psnr / len(dataset)
99
+ sigma_int = int(round(sigma * 255.0))
100
+ print(
101
+ f"[+] {dataset_name} sigma={sigma_int}/255 PSNR: {avg_psnr:.2f} dB "
102
+ f"({len(dataset)} images)"
103
+ )
104
+ return avg_psnr
105
+
106
+
107
+ def main():
108
+ print("[*] Classical Majee 2020 baseline — Set12 & BSD68")
109
+ for dataset_name in ("Set12", "BSD68"):
110
+ for sigma in _SIGMAS:
111
+ run_eval(dataset_name, sigma)
112
+ print()
113
+
114
+
115
+ if __name__ == "__main__":
116
+ main()
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datasets/DIV2K_Train_HR/DIV2K_train_HR/0033.png ADDED

Git LFS Details

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  • Size of remote file: 4.19 MB
datasets/DIV2K_Train_HR/DIV2K_train_HR/0034.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0035.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0036.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0037.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0038.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0039.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0040.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0041.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0042.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0043.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0044.png ADDED

Git LFS Details

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datasets/DIV2K_Train_HR/DIV2K_train_HR/0045.png ADDED

Git LFS Details

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