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