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dfe630b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | # Cloud Removal Visualization & Evaluation
Benchmark evaluation workspace for the **DiffCR** paper
(*Diffusion-Based Cloud Removal for Sentinel-2 Multi-Temporal Imagery*).
Two test datasets are covered:
| Dataset | Samples | Methods |
|---|---|---|
| Sen2\_MTC\_Old | 313 | 12 |
| Sen2\_MTC\_New | 687 | 12 |
---
## Directory Layout
```
visualization/
├── paper-report.png ← reference metrics table from the paper
│
├── data/
│ ├── Sen2_MTC_New/
│ │ ├── GT/ ← 687 cloud-free ground-truth images ({id}.png)
│ │ └── inputs/ ← 687 × 3 cloudy input images
│ │ ({id}_A1.png {id}_A2.png {id}_A3.png)
│ └── Sen2_MTC_Old/
│ ├── GT/ ← 313 ground-truth images
│ └── inputs/ ← 313 × 3 cloudy inputs
│
├── results/
│ ├── Sen2_MTC_New/
│ │ ├── ae/ ← prediction images for each method ({id}.png)
│ │ ├── crtsnet/
│ │ ├── ctgan/
│ │ ├── ddpmcr/
│ │ ├── diffcr/ ← DiffCR [Ours]
│ │ ├── dsen2cr/
│ │ ├── mcgan/
│ │ ├── pix2pix/
│ │ ├── pmaa/
│ │ ├── stgan/
│ │ ├── stnet/
│ │ └── uncrtaints/
│ └── Sen2_MTC_Old/
│ └── (same 12 methods)
│
└── eval/
├── metrics.py ← PSNR / SSIM / FID / LPIPS evaluation
├── plot.py ← comparison figure generation
└── requirements.txt ← Python dependencies
```
---
## Quick Start
### 1. Install dependencies
```bash
pip install -r eval/requirements.txt
```
> **CUDA note** – SSIM uses the 3-D Gaussian kernel from the paper, which
> requires a CUDA-enabled PyTorch installation to reproduce the exact paper
> values. PSNR, FID and LPIPS are fully reproducible on CPU.
> Install the correct torch wheel for your GPU from https://pytorch.org.
---
### 2. Run evaluation
```bash
# Evaluate all 12 methods on both datasets (prints a full summary table):
python eval/metrics.py
# One specific method:
python eval/metrics.py --method diffcr
# One specific dataset:
python eval/metrics.py --dataset Sen2_MTC_New
# One method + one dataset:
python eval/metrics.py --dataset Sen2_MTC_Old --method diffcr
# Fast check (skip FID and LPIPS):
python eval/metrics.py --no-fid --no-lpips
# Arbitrary directory pair:
python eval/metrics.py --gt /path/to/GT --pred /path/to/Out
```
Expected output (excerpt, requires CUDA for exact SSIM):
```
Method | Sen2_MTC Old | Sen2_MTC New
| PSNR SSIM FID LPIPS | PSNR SSIM FID LPIPS
--------------------------------------------------------------------------------
...
diffcr | 29.112 0.886 89.845 0.258 | 19.150 0.671 83.162 0.291
```
---
### 3. Generate comparison figures
```bash
# Generate the exact figures used in the paper:
python eval/plot.py --paper-samples
# Paper figures for one dataset:
python eval/plot.py --paper-samples --dataset Sen2_MTC_New
python eval/plot.py --paper-samples --dataset Sen2_MTC_Old
# Any specific sample:
python eval/plot.py --dataset Sen2_MTC_New --id T12TUR_R027_55
# List all available sample IDs:
python eval/plot.py --dataset Sen2_MTC_New --list
# Generate figures for every sample:
python eval/plot.py --dataset Sen2_MTC_New --all
```
Figures are saved as PDF to `eval/plots/` by default.
---
## Methods
| Method | Venue | Abbrev |
|---|---|---|
| MCGAN | CVPRW 2017 | mcgan |
| Pix2Pix | CVPR 2017 | pix2pix |
| AE | ECTI-CON 2018 | ae |
| STNet | TGRS 2020 | stnet |
| DSen2-CR | ISPRS J PHOTOGRAM 2020 | dsen2cr |
| STGAN | WACV 2020 | stgan |
| CTGAN | ICIP 2022 | ctgan |
| CR-TS-Net | TGRS 2022 | crtsnet |
| PMAA | arXiv 2023 | pmaa |
| UnCRtainTS | CVPRW 2023 | uncrtaints |
| DDPM-CR | Remote Sensing 2023 | ddpmcr |
| **DiffCR [Ours]** | **TGRS 2024** | **diffcr** |
---
## Paper Results
---
## Notes
- All images use the unified naming scheme `{id}.png` (GT and predictions)
and `{id}_A{1,2,3}.png` (cloudy inputs).
- `results/Sen2_MTC_Old/diffcr/` images are stored in their original
coordinate convention; `eval/plot.py` applies a horizontal flip
automatically when rendering the Old-dataset comparison figure so that
all panels share a consistent visual orientation.
- `migrate.py` in the project root was the one-time script used to produce
the current layout from the original raw experiment directories.
It is kept for reference but does not need to be re-run. |