---
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.