astrosr 1.0.1

Astronomical super-resolution with drizzle for wide field-of-view images

astroSR - Astronomical Super-Resolution with Drizzle

Description

This package implements the Drizzle algorithm (Fruchter & Hook 2002) to combine multiple dithered astronomical images and create super-resolution images with precise photometry conservation.

Key Features

✨ Professional Drizzle Algorithm

• Faithful implementation of Fruchter & Hook (2002)
• Exact flux conservation for precise photometry
• Pixel-to-pixel mapping with overlap area calculation

🔬 Real Resolution Improvement

• Increases spatial resolution (reduces arcsec/pix)
• Leverages dithering to resolve sub-pixel details
• Configurable improvement factor (typically 2-3×)

📊 Flexible Weighting

• Custom weights per image (exposure time, quality, etc.)
• Multiple kernel types (square, gaussian, tophat)
• Noise correlation control via pixfrac parameter

📐 Complete WCS Support

• Input WCS header handling
• Precise geometric transformations
• Output image WCS generation

Resolution Gain with Drizzle

The achievable spatial resolution using drizzle depends on:

• The number of input images per field.
• The dithering pattern (relative sub-pixel shifts between images).
• The alignment quality and the PSF.

Theoretical estimate:

• If images are well-aligned and dithers are optimal, the pixel scale can be reduced by up to a factor of √N, where N is the number of independent, well-dithered images.
• In practice, the gain is usually between 1.5× and 2× (i.e., you can halve the pixel scale) with at least 4–9 images and good dithering.

Practical summary:

• 2 images: modest improvement (~1.4×)
• 4 images: clear improvement (~2×)
• 9 images: optimal improvement (~3×)
• More images: marginal additional gain, limited by PSF and alignment.

Installation

# Clone the repository
git clone https://github.com/dot-gabriel-ferrer/astroSR.git
cd astroSR

# Install
pip install .

# Or for development
pip install -e .

Dependencies

• Python ≥ 3.9
• NumPy
• Astropy
• SciPy
• Matplotlib (for visualization)

Quick Usage

Command Line

# Basic example: combine 4 images with 2× improvement factor
python scripts/run_drizzle.py \
    -i img1.fits img2.fits img3.fits img4.fits \
    -o combined_highres.fits \
    --scale-factor 2.0 \
    --pixfrac 0.8

# With custom weights
python scripts/run_drizzle.py \
    -i short.fits long.fits \
    -o weighted_output.fits \
    --weights 1.0 3.0 \
    --scale-factor 2.0

As Python Module

from astrosr import drizzle_super_resolution

# Basic example
drizzle_super_resolution(
    input_files=['img1.fits', 'img2.fits', 'img3.fits'],
    output_file='superres.fits',
    scale_factor=2.0,
    pixfrac=0.8
)

# With advanced parameters
drizzle_super_resolution(
    input_files=['obs1.fits', 'obs2.fits', 'obs3.fits', 'obs4.fits'],
    output_file='output.fits',
    pixel_scale=0.5,        # arcsec/pix
    scale_factor=2.0,
    pixfrac=0.8,
    weights=[1.0, 1.0, 1.2, 0.8],
    kernel='square',
    save_weight_map=True,
    preserve_flux=True
)

Documentation

• Usage Guide: Detailed examples and use cases
• Drizzle Algorithm: Mathematical foundations and references
• API Reference: Complete function documentation (coming soon)

Main Parameters

Parameter	Default	Description
scale_factor	2.0	Resolution improvement factor (1.5-3.0 typical)
pixfrac	0.8	Drizzle pixel fraction (0.6-1.0 recommended)
kernel	'square'	Kernel type: square, gaussian, tophat
weights	None	Per-image weights (e.g., exposure time)
pixel_scale	auto	Output pixel scale (arcsec/pix)

See docs/usage.md for detailed descriptions.

Use Cases

1. Dithered Observations

Combine multiple exposures of the same field with small displacements:

python scripts/run_drizzle.py -i dithered_*.fits -o superres.fits --scale-factor 2.0

2. Overlapping Mosaics

Join partially overlapping images into a high-resolution mosaic:

python scripts/run_drizzle.py -i tile_*.fits -o mosaic.fits --scale-factor 1.5

3. Quality Weighting

Assign weights based on exposure time or seeing:

drizzle_super_resolution(
    input_files=['good_seeing.fits', 'medium_seeing.fits', 'poor_seeing.fits'],
    output_file='quality_weighted.fits',
    weights=[3.0, 2.0, 1.0],
    scale_factor=2.0
)

Project Structure

astrosr/
├── src/astrosr/              # Main code
│   ├── __init__.py
│   └── drizzle_super_resolution.py
├── tests/                    # Unit tests
│   └── test_drizzle_super_resolution.py
├── scripts/                  # CLI scripts
│   └── run_drizzle.py
├── docs/                     # Documentation
│   ├── usage.md             # Usage guide
│   └── algorithm.md         # Mathematical foundations
├── examples/                 # Examples (coming soon)
├── pyproject.toml           # Package configuration
└── README.md                # This file

Scientific References

1. Fruchter, A. S., & Hook, R. N. (2002). "Drizzle: A Method for the Linear Reconstruction of Undersampled Images". Publications of the Astronomical Society of the Pacific, 114(792), 144-152. DOI: 10.1086/338393

2. Gonzaga, S., et al. (2012). "The DrizzlePac Handbook". Space Telescope Science Institute.

3. Koekemoer, A. M., et al. (2003). "HST Dither Handbook". Space Telescope Science Institute.

Validation

The algorithm has been validated for:

• ✓ Flux conservation (<1% error)
• ✓ Effective resolution improvement
• ✓ Correct NaN pixel handling
• ✓ Precise geometric transformations

See tests/ for test cases.

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

MIT License - see LICENSE file for details.

Contact

• Issues: GitHub Issues
• Email: [e-mail]

Acknowledgments

• Original algorithm: Andrew Fruchter and Richard Hook (STScI)
• Inspiration: DrizzlePac package from Space Telescope Science Institute
• Astropy community

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Note: This is a research package. For critical applications, consider using the official DrizzlePac from STScI.