# 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

![Paper metrics table](paper-report.png)

---

## 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.