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sampled_data/x2/train_hr_patch/hst_10152_05_acs_wfc_f814w_j90i05_drc_padded_hr_hr_patch_0.npy
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sampled_data/x2/train_hr_patch/hst_10152_05_acs_wfc_f814w_j90i05_drc_padded_hr_hr_patch_1.npy
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sampled_data/x2/train_hr_patch/hst_10152_05_acs_wfc_f814w_j90i05_drc_padded_hr_hr_patch_100.npy
sampled_data/x2/train_lr_patch/hst_10152_05_acs_wfc_f814w_j90i05_drc_padded_hr_lr_patch_100.npy
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sampled_data/x2/train_lr_patch/hst_10152_05_acs_wfc_f814w_j90i05_drc_padded_hr_lr_patch_102.npy
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sampled_data/x2/train_lr_patch/hst_10152_05_acs_wfc_f814w_j90i05_drc_padded_hr_lr_patch_103.npy
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sampled_data/x2/train_hr_patch/hst_10152_05_acs_wfc_f814w_j90i05_drc_padded_hr_hr_patch_108.npy
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End of preview. Expand in Data Studio

STAR Dataset (Super-Resolution for Astronomical Star Fields)

The STAR dataset is a large-scale benchmark for developing field-level super-resolution models in astronomy. It contains 54,738 flux-consistent image pairs derived from Hubble Space Telescope (HST) high-resolution observations and physically faithful low-resolution counterparts.

🌟 Key Features

  • Flux Consistency: Ensures consistent flux using a flux-preserving data generation pipeline
  • Object-Crop Configuration: Strategically samples patches across diverse celestial regions
  • Data Diversity: Covers dense star clusters, sparse galactic fields, and regions with varying background noise

πŸ“Š Dataset Structure

KUOCHENG/STAR/
β”œβ”€β”€ sampled_data/x2/              #  ⚠️ SAMPLE ONLY - For testing/exploration(600 samples). You can get started quickly with the data here.
β”‚   β”œβ”€β”€ train_hr_patch/           # 500 HR training patches (.npy files)
β”‚   β”œβ”€β”€ train_lr_patch/           # 500 LR training patches (.npy files)
β”‚   β”œβ”€β”€ eval_hr_patch/            # 100 HR validation patches (.npy files)
β”‚   β”œβ”€β”€ eval_lr_patch/            # 100 LR validation patches (.npy files)
β”‚   β”œβ”€β”€ train_metadata.jsonl      # Training pairs metadata
β”‚   └── validation_metadata.jsonl # Validation pairs metadata
└── data/
    β”œβ”€β”€ x2/x2.tar.gz              # Full x2 dataset (33GB)
    └── x4/x4.tar.gz              # Full x4 dataset (29GB)

⚠️ Important Note: The sampled_data/ directory contains only a small subset (600 pairs) for quick testing and understanding the data structure. For actual training and research, please use the full datasets in data/ directory.

πŸš€ Quick Start

Loading the Dataset

from datasets import load_dataset
import numpy as np

# Load metadata
dataset = load_dataset("KUOCHENG/STAR")

# Access a sample
sample = dataset['train'][0]
hr_path = sample['hr_path']  # Path to HR .npy file
lr_path = sample['lr_path']  # Path to LR .npy file

# Load actual data
hr_data = np.load(hr_path, allow_pickle=True).item()
lr_data = np.load(lr_path, allow_pickle=True).item()

Understanding the Data Format

Each .npy file contains a dictionary with the following structure:

High-Resolution (HR) Data

  • Shape: (256, 256) for all HR patches
  • Access Keys:
    hr_data['image']     # The actual grayscale astronomical image
    hr_data['mask']      # Binary mask (True = valid/accessible pixels)
    hr_data['attn_map']  # Attention map from star finder (detected astronomical sources)
    hr_data['coord']     # Coordinate information (if available)
    

Low-Resolution (LR) Data

  • Shape: Depends on the super-resolution scale
    • For x2: (128, 128)
    • For x4: (64, 64)
  • Access Keys: Same as HR data
    lr_data['image']     # Downsampled grayscale image
    lr_data['mask']      # Downsampled mask
    lr_data['attn_map']  # Downsampled attention map
    lr_data['coord']     # Coordinate information
    

Data Fields Explanation

Field Description Type Usage
image Raw astronomical observation data np.ndarray (float32) Main input for super-resolution
mask Valid pixel indicator np.ndarray (bool) Identifies accessible regions (True = valid)
attn_map Star finder output np.ndarray (float32) Highlights detected astronomical sources (stars, galaxies)
coord Spatial coordinates np.ndarray Position information for patch alignment

πŸ’» Usage Examples

Basic Training Loop

⚠️NOTE: Complete training and testing code/framework, please see github.

import numpy as np
from datasets import load_dataset
import torch
from torch.utils.data import DataLoader

# Load dataset
dataset = load_dataset("KUOCHENG/STAR")

class STARDataset(torch.utils.data.Dataset):
    def __init__(self, hf_dataset):
        self.dataset = hf_dataset
    
    def __len__(self):
        return len(self.dataset)
    
    def __getitem__(self, idx):
        sample = self.dataset[idx]
        
        # Load .npy files
        hr_data = np.load(sample['hr_path'], allow_pickle=True).item()
        lr_data = np.load(sample['lr_path'], allow_pickle=True).item()
        
        # Extract images
        hr_image = hr_data['image'].astype(np.float32)
        lr_image = lr_data['image'].astype(np.float32)
        
        # Extract masks for loss computation
        hr_mask = hr_data['mask'].astype(np.float32)
        lr_mask = lr_data['mask'].astype(np.float32)
        
        # Convert to tensors
        return {
            'lr_image': torch.from_numpy(lr_image).unsqueeze(0),  # Add channel dim
            'hr_image': torch.from_numpy(hr_image).unsqueeze(0),
            'hr_mask': torch.from_numpy(hr_mask).unsqueeze(0),
            'lr_mask': torch.from_numpy(lr_mask).unsqueeze(0),
        }

# Create PyTorch dataset and dataloader
train_dataset = STARDataset(dataset['train'])
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# Training loop example
for batch in train_loader:
    lr_images = batch['lr_image']  # [B, 1, 128, 128] for x2
    hr_images = batch['hr_image']  # [B, 1, 256, 256]
    masks = batch['hr_mask']       # [B, 1, 256, 256]
    
    # Your training code here
    # pred = model(lr_images)
    # loss = criterion(pred * masks, hr_images * masks)  # Apply mask to focus on valid regions

Visualization

πŸ”­ Astronomical Image Visualization

Astronomical images have extreme dynamic ranges with both very bright stars and faint background features. Direct visualization often shows only the brightest sources. We need special normalization techniques for proper visualization.

import matplotlib.pyplot as plt
import numpy as np
from astropy.visualization import ZScaleInterval, ImageNormalize

# If astropy is not installed: pip install astropy

def z_scale_normalize(image, contrast=0.25):
    """
    Apply Z-scale normalization for astronomical images.
    This technique enhances faint features while preventing bright stars from saturating.
    
    Args:
        image: Input astronomical image
        contrast: Contrast parameter (default 0.25, lower = more contrast)
    
    Returns:
        Normalized image suitable for visualization
    """
    # Remove NaN and Inf values
    image_clean = np.nan_to_num(image, nan=0.0, posinf=0.0, neginf=0.0)
    
    interval = ZScaleInterval(contrast=contrast)
    vmin, vmax = interval.get_limits(image_clean)
    norm = ImageNormalize(vmin=vmin, vmax=vmax)
    return norm(image_clean)

def visualize_sample(hr_path, lr_path):
    # Load data
    hr_data = np.load(hr_path, allow_pickle=True).item()
    lr_data = np.load(lr_path, allow_pickle=True).item()
    
    fig, axes = plt.subplots(2, 3, figsize=(15, 10))
    hr_image_vis = z_scale_normalize(hr_data)
    lr_image_vis = z_scale_normalize(lr_data)

    # HR visualizations
    axes[0, 0].imshow(hr_data['image'], cmap='gray')
    axes[0, 0].set_title('HR Image (256x256)')
    
    axes[0, 1].imshow(hr_data['mask'], cmap='binary')
    axes[0, 1].set_title('HR Mask (Valid Regions)')
    
    axes[0, 2].imshow(hr_data['attn_map'], cmap='hot')
    axes[0, 2].set_title('HR Attention Map (Detected Sources)')
    
    # LR visualizations
    axes[1, 0].imshow(lr_data['image'], cmap='gray')
    axes[1, 0].set_title(f'LR Image ({lr_data["image"].shape[0]}x{lr_data["image"].shape[1]})')
    
    axes[1, 1].imshow(lr_data['mask'], cmap='binary')
    axes[1, 1].set_title('LR Mask')
    
    axes[1, 2].imshow(lr_data['attn_map'], cmap='hot')
    axes[1, 2].set_title('LR Attention Map')
    
    plt.tight_layout()
    plt.show()

# Visualize a sample
sample = dataset['train'][0]
visualize_sample(sample['hr_path'], sample['lr_path'])

πŸ“ File Naming Convention

  • HR files: *_hr_hr_patch_*.npy
  • LR files: *_hr_lr_patch_*.npy

Files are paired by replacing _hr_hr_patch_ with _hr_lr_patch_ in the filename.

πŸ”„ Full Dataset Access

For the complete dataset (54,738 pairs), download the compressed files:

# Manual download and extraction
import tarfile

# Extract x2 dataset
with tarfile.open('data/x2/x2.tar.gz', 'r:gz') as tar:
    tar.extractall('data/x2/')

# The extracted structure will be:
# data/x2/
#   β”œβ”€β”€ train_hr_patch/  # ~45,000 HR patches
#   β”œβ”€β”€ train_lr_patch/  # ~45,000 LR patches
#   β”œβ”€β”€ eval_hr_patch/   # ~9,000 HR patches
#   β”œβ”€β”€ eval_lr_patch/   # ~9,000 LR patches
#   β”œβ”€β”€ dataload_filename/
#   β”‚   β”œβ”€β”€ train_dataloader.txt  # Training pairs list
#   β”‚   └── eval_dataloader.txt   # Evaluation pairs list
#   └── psf_hr/, psf_lr/  # Original unpatched data

🎯 Model Evaluation Metrics

When evaluating super-resolution models on STAR, consider:

  1. Masked PSNR/SSIM: Only compute metrics on valid pixels (where mask=True)
  2. Source Detection F1: Evaluate if astronomical sources are preserved
  3. Flux Preservation: Check if total flux is maintained (important for astronomy, see the paper for details)

πŸ“ Citation

If you use the STAR dataset in your research, please cite:

@article{wu2025star,
  title={STAR: A Benchmark for Astronomical Star Fields Super-Resolution},
  author={Wu, Kuo-Cheng and Zhuang, Guohang and Huang, Jinyang and Zhang, Xiang and Ouyang, Wanli and Lu, Yan},
  journal={arXiv preprint arXiv:2507.16385},
  year={2025},
  url={https://arxiv.org/abs/2507.16385}
}

πŸ“„ License

This dataset is released under the MIT License.

🀝 Contact

For questions or issues, please open an issue on the dataset repository. Also can see github

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