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pretty_name: MuSCAT RawImage
license: cc-by-nc-4.0
viewer: false
tags:
- astronomy
- image
- remote-sensing
- scientific-data
- fits
- raw
- denoising
- astrophotography
- ccd
task_categories:
- image-to-image
language:
- en
---
# Dataset Card for MuSCAT-RawImage
## Dataset Description
- **Dataset name:** MuSCAT-RawImage
- **Repository:** `ToferFish/MuSCAT-RawImage`
- **Source project:** [ShuhongLL/Denoising-Deep-Sky](https://github.com/ShuhongLL/Denoising-Deep-Sky)
- **License:** CC BY-NC 4.0
- **Modality:** Astronomical raw CCD images and calibration products
- **Format:** Compressed FITS files (`.fits.fz`) plus JSON metadata
This dataset contains raw and processed astronomical CCD observations from the MuSCAT instruments, organized for deep-sky image denoising and related low-level image restoration research.
The repository structure follows the organization used in the Denoising-Deep-Sky project, where data are grouped by **instrument**, **band**, and **observation session**.
The data are intended primarily for:
- astronomical image denoising
- raw-to-clean image restoration
- CCD noise modeling
- calibration-aware image processing
- benchmarking denoising methods on real telescope data
## Dataset Structure
The dataset is organized by instrument, spectral band, and observation session.
A typical directory layout is:
```text
dataset_root/
├── muscat3_g/
│ ├── <observation_id_1>/
│ │ ├── calib/
│ │ │ ├── BIAS_*.fits.fz
│ │ │ ├── BPM_*.fits.fz
│ │ │ ├── DARK_*.fits.fz
│ │ │ └── SKYFLAT_*.fits.fz
│ │ ├── data/
│ │ │ ├── <obs_id>_calib.fits.fz
│ │ │ ├── <obs_id>_mask.fits.fz
│ │ │ ├── <obs_id>_mean.fits.fz
│ │ │ ├── <obs_id>_os.fits.fz
│ │ │ └── <obs_id>_raw.fits.fz
│ │ ├── e91/
│ │ ├── calib.json
│ │ └── info.json
│ └── <observation_id_2>/
│ └── ...
└── muscat4_i/
└── ...
```
## Data Contents
Each observation directory may include the following components.
### Calibration files (`calib/`)
- `BIAS_*.fits.fz`
Master bias frame. Represents the electronic offset estimate.
- `BPM_*.fits.fz` (Empty)
Bad pixel map.
- `DARK_*.fits.fz`
Master dark-current template.
- `SKYFLAT_*.fits.fz`
Sky flat used for pixel-response and vignetting correction.
### Observation data (`data/`)
- `<obs_id>_raw.fits.fz`
Raw CCD frame.
- `<obs_id>_os.fits.fz`
Overscan-corrected frame, typically used as the noisy input.
- `<obs_id>_calib.fits.fz`
Calibrated frame.
- `<obs_id>_mean.fits.fz`
High-SNR stacked reference frame built from multiple exposures.
- `<obs_id>_mask.fits.fz`
Background/source mask.
### Additional files
- `e91/`
Additional calibrated frames used to construct stacked references.
- `calib.json`
Calibration-related metadata.
- `info.json`
Observation/session metadata.
## Data Fields
This repository does not follow a tabular row-based structure.
Instead, each sample is stored as a set of FITS files and JSON metadata under one observation directory.
Important file-level fields include:
- **instrument**: e.g. MuSCAT-3 or MuSCAT-4
- **band**: e.g. `g`, `r`, `i`
- **observation_id**: identifier for one observation session
- **raw frame**: original CCD image
- **overscan-corrected frame**: original CCD image - overscan
- **calibrated frame**: processed image using calibration products
- **mean frame**: stacked high-SNR reference image
- **mask**: source/background mask (background = 1, source = 0)
- **calibration metadata**: gain, dark, bias, flat-related information when available
## Supported Tasks and Use Cases
This dataset is suitable for:
- real-world astronomical image denoising
- supervised image-to-image restoration
- physics-based CCD noise modeling
- calibration-aware low-level vision
- benchmarking telescope image restoration pipelines
Possible training pairs include:
- original pairs: `*_os.fits.fz` ----> `*_calib.fits.fz`
- synthesized pairs: `*_syn.fits.fz` ----> `*_mean.fits.fz`
Depending on the experiment design, users may also incorporate:
- `*_raw.fits.fz`
- `*_calib.fits.fz`
- calibration frames from `calib/`
- masks from `*_mask.fits.fz`
## Loading the Data
Because the dataset is distributed as FITS files instead of a standard row-based file format such as CSV or Parquet, it is generally intended to be accessed as files from the repository rather than directly through the default Hugging Face dataset viewer.
Example with Python:
```python
from astropy.io import fits
path = "muscat3_g/<observation_id>/data/<obs_id>_raw.fits.fz"
with fits.open(path) as hdul:
image = hdul[1].data
header = hdul[1].header
print(image.shape)
print(header)
```
## Citation
If you use this dataset, please cite the original project:
```bibtex
@misc{liu2025denoisingdeepsky,
title={Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging},
author={Shuhong Liu and Xining Ge and Ziying Gu and Quanfeng Xu and Ziteng Cui and Lin Gu and Xuangeng Chu and Jun Liu and Dong Li and Tatsuya Harada},
year={2025},
eprint={2508.08079},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |