Pylira: deconvolution of images in the presence of Poisson noise
Douglas Burke
Karthik Reddy Solipuram
David van Dyk
All physical and astronomical imaging observations are degraded by the finite angular
resolution of the camera and telescope systems. The recovery of the true image is limited by
both how well the instrument characteristics are known and by the magnitude of measurement noise.
In the case of a high signal to noise ratio data, the image can be sharpened or “deconvolved” robustly
by using established standard methods such as the Richardson-Lucy method. However, the situation changes
for sparse data and the low signal to noise regime, such as those frequently encountered in
X-ray and gamma-ray astronomy, where deconvolution leads inevitably to an amplification
of noise and poorly reconstructed images. However, the results in this regime can be improved
by making use of physically meaningful prior assumptions and statistically principled
modeling techniques. One proposed method is the LIRA algorithm, which
requires smoothness of the reconstructed image at multiple scales. In this contribution, we
introduce a new python package called Pylira, which exposes the original C implementation
of the LIRA algorithm to Python users. We briefly describe the package structure, development
setup and show a Chandra as well as Fermi-LAT analysis example.
deconvolution, point spread function, poisson, low counts, X-ray, gamma-ray
DOI10.25080/majora-212e5952-00f