| --- |
| license: mit |
| tags: |
| - diffusion |
| - lidar |
| - super-resolution |
| - denoising |
| - remote-sensing |
| - satellite |
| - compressed-sensing |
| datasets: |
| - anfera236/HHDC |
| --- |
| |
| # HHDC-2m — Diffusion Model for Satellite LiDAR Reconstruction |
|
|
| Pretrained checkpoint for the paper: |
|
|
| > **Diffusion-Based Joint Recovery, Denoising, and Super-Resolution of Compressed-Sensing Satellite LiDAR Data** |
| > Andres Ramirez-Jaime, Nestor Porras-Diaz, Mark Stephen, Guangning Yang, Gonzalo R. Arce |
| > University of Delaware · NASA Goddard Space Flight Center |
|
|
| ## What this model does |
|
|
| A Gaussian Diffusion U-Net trained to jointly reconstruct, denoise, and super-resolve 3D canopy volume data from compressed-sensing satellite LiDAR acquisitions (HHDC instrument). The inference pipeline uses Diffusion Posterior Sampling (DPS) with a physics-based Poisson forward imaging model as the data-consistency constraint. |
|
|
| - **Resolution:** 2× super-resolution (`model2.pt`) |
| - **Architecture:** `Unet(dim=128, dim_mults=(8, 16, 16, 16), flash_attn=True, channels=128)` |
| - **Diffusion:** Gaussian diffusion, 1000 training timesteps, DDIM sampling (250 steps default) |
| - **Guidance:** DPS gradient-based guidance enforcing Poisson log-likelihood |
|
|
| ## Quickstart |
|
|
| ```bash |
| # 1. Clone the inference repo |
| git clone https://github.com/Anfera/DenoisSuperResOfCSHHDC.git |
| cd DenoisSuperResOfCSHHDC |
| |
| # 2. Install dependencies (Python 3.10+ recommended) |
| pip install -r requirements.txt |
| |
| # 3. Download the checkpoint |
| mkdir -p results |
| hf download anfera236/HHDC-2m model2.pt --local-dir results/ |
| |
| # 4. Place test data in data/TestCube/ (gt2.npy is provided in the repo) |
| |
| # 5. Run inference |
| python SingleLikelihood.py |
| ``` |
|
|
| Outputs are saved to `resultCubes/` (final reconstructions) and `intermediateCubesTest/` (DDIM snapshots). |
|
|
| ## Configuration |
|
|
| All tunable parameters live in `src/config.py`: resolution, DDIM steps, mask type (`blue_noise` / `random` / `bayer`), sampling ratio, physics model parameters (footprint diameter, background rate, readout noise), and output paths. |
|
|
| ## Dataset |
|
|
| Test data and full dataset: [anfera236/HHDC](https://huggingface.co/datasets/anfera236/HHDC) |
|
|
| ## Funding |
|
|
| Supported by U.S. National Science Foundation Grant No. 2404740 and NASA Grant No. 80NSSC25K7395. |
|
|
