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metadata 
license: cc-by-nc-4.0
task_categories:
  - image-to-image
tags:
  - astronomy
  - super-resolution
  - atmospheric-turbulence
  - cassini
size_categories:
  - 10K<n<100K

ASTRA-SR Dataset

Dataset and the CONTROL checkpoint accompanying ASTRA-SR: Atmospheric Seeing and Turbulence Restoration for Astronomical Image Super-Resolution by Xining Ge, Ziteng Cui and Shuhong Liu.

Code and instructions · Checkpoint

Release

All train, validation and test splits are available: 66,293 samples in 134 tar.gz shards, preserving float32 FITS precision. The data release at revision 554f85202f20b94e2e569b6d2d969adf4f344933 contains 277 files and 136,946,620,648 bytes including metadata and weights. Subsequent documentation and helper-script changes do not change the pinned data revision.

Split real png Samples Shards
Train 57,860 5,722 63,582 126
Validation 677 678 1,355 4
Test 678 678 1,356 4

real and png are source-kind labels inherited from the experiment. The paper describes Cassini ISS clean sources, turbulence strengths from MASS measurements at {0.5,1,2,4,8,16} km, spatially varying PSFs and Gaussian read noise. Every row includes its protocol and PSF hashes.

Splits are disjoint by source_id. A 2026-10-07 scan found no repeated IDs within splits or shared IDs across splits. This does not establish independence between related observation sequences or differently named source images. Paper Table 1 uses validation, not test, with fixed epoch-20 selection.

Download and prepare

Recommended: use the companion code repository, which includes both helper files:

git clone https://github.com/I2WM/ASTRA-SR.git
cd ASTRA-SR
python -m pip install -r requirements.txt
python scripts/prepare_dataset.py --splits val --local-dir astrasr_data --artifacts
# Full training data and epoch-end validation:
python scripts/prepare_dataset.py --splits train val --local-dir astrasr_data

For the standalone HF helper, download prepare_dataset.py and release_utils.py from the current repository version into one directory. The helper itself pins archive/metadata downloads to revision 554f852.... Use Python 3.12 and install huggingface_hub before running it.

hf download xiningning/astrasr_data prepare_dataset.py release_utils.py \
  --repo-type dataset --local-dir astra-tools
python astra-tools/prepare_dataset.py --splits val --local-dir astrasr_data

Each requested archive is checked against the pinned checksum manifest before extraction. Only the requested splits are processed, including when other splits are already cached. Archives remain on disk unless --remove-archives is explicitly selected. --download-only skips extraction; --local-only verifies and extracts an existing download without network requests.

The current helper also downloads and verifies LICENSE, LICENSE_SCOPE.md, DATA_SOURCES.md and THIRD_PARTY_NOTICES.md from licensing revision 6c1228d752a229ed80d63d49a0d8c012f2fa084c. The original data revision and this notice revision are pinned separately. For an older local download that lacks these notices, run the current helper normally once before using --local-only.

The helper writes a local records_val.jsonl, records_train_val.jsonl, etc. with paths rebased to your extraction directory. It does not modify the immutable public index and does not pretend the derived index retains the historical server index's hash.

Array format and layout

Every archive extracts to <split>/<kind>/<role>/<sample_name>.fits:

Role Shape Use
clean_hr512 512×512 Clean high-resolution target
clean_lr256 256×256 Clean low-resolution diagnostic
degraded_lr256 256×256 Sole model input
psf_only_lr256 256×256 Intermediate supervision / diagnostic
noise_only_lr256 256×256 Noise-only diagnostic
noise_map_lr256 256×256 Pre-clipping noise realization

The model scales FITS intensity by 1/2500. Extra arrays support training and analysis; they are not inference conditioning inputs.

  • dataset_index.jsonl: 66,293 rows with relative sample paths, source IDs, split/kind, seeds and protocol/PSF hashes.
  • x2_dataset_protocol_lrdegrade_v2.json: immutable scientific protocol.
  • source_manifest_strict_v2_x2_v1.jsonl: historical source-construction manifest. It retains original server paths as provenance, not runnable local paths.
  • SHA256SUMS.txt: all 134 archive hashes; each archive also has a .sha256.
  • checkpoints/astra_sr_control_epoch20.pt: verified epoch-20 checkpoint (60,911,427 bytes), SHA-256 79adb6c8dc158d7d0365fa9d21178fa0d32f7c65ffa8a7bca64436343bfa2fad.
  • psf_bank/psf_row_34239_M5_d0.333.npy: example 32×32 field of 33×33 kernels, SHA-256 c09a24b516fc048559cd26a28e913e329972e44c948916299549a05db6404285.

The single PSF file is not the complete population referenced by every sample. Rebuilding every array from scratch also requires original clean source collections, the MASS CSV and the corresponding PSF population; all of these inputs are not bundled in this release.

Validation limits

All archive hashes were compared with HF LFS SHA-256 object IDs, and the full public index was checked. This was metadata verification, not a re-download and re-evaluation of the entire dataset. The portable training and evaluation adapters have not completed a new 20-epoch training/full-validation reproduction. See the code repository's docs/RELEASE_SCOPE.md for scope and validation.

License: noncommercial use

The authors' code, released model weights and dataset contributions are offered under CC BY-NC 4.0. Attribution is required; commercial use is not granted by this license. Retain source notices, link the license and indicate modifications. The full LICENSE and LICENSE_SCOPE.md accompany this dataset and the companion code. Upstream materials keep their applicable terms. This update does not revoke permissions validly granted under licenses accompanying earlier versions.

Data sources: Cassini ISS / NASA

The Cassini-derived real branch originates from the Cassini Imaging Science Subsystem (ISS). Source observations are credited to NASA / JPL-Caltech / Space Science Institute; the official mission and archive references are:

ASTRA-SR adds image curation/preprocessing, paired resampling, synthesized spatially varying turbulence blur and Gaussian noise, and split metadata. The released FITS arrays are processed derivatives, not an unmodified NASA archive or a NASA-endorsed benchmark. Credit the original observations and the ASTRA-SR authors and paper when reusing them.

DATA_SOURCES.md contains a reusable credit line and processing details. The auxiliary png branch has a separate source history; its complete upstream attribution/license mapping remains pending maintainer verification. Original NASA/Cassini source terms are not replaced by the project's license. See THIRD_PARTY_NOTICES.md and LICENSE_SCOPE.md for the scope of the grant.

Citation 

@misc{ge2026astrasr,
  title = {ASTRA-SR: Atmospheric Seeing and Turbulence Restoration for Astronomical Image Super-Resolution},
  author = {Xining Ge and Ziteng Cui and Shuhong Liu},
  year = {2026},
  eprint = {2609.26731},
  archivePrefix = {arXiv},
  primaryClass = {cs.CV},
  url = {https://arxiv.org/abs/2609.26731}
}