Cleaning Images with Gaussian Process Regression

TL;DR

This paper introduces astrofix, a Gaussian Process Regression-based algorithm for imputing missing or corrupted pixels in astronomical images, improving data quality across various instruments and data types.

Contribution

The paper presents astrofix, a novel, adaptive image imputation method that automatically selects optimal kernels for Gaussian Process Regression to handle diverse astronomical imaging data.

Findings

Astrofix significantly reduces mean absolute error compared to median and Gaussian kernel methods.

Demonstrates effective performance on imaging and spectroscopic data from multiple telescopes.

Handles clusters of bad pixels and image edges robustly.

Abstract

Many approaches to astronomical data reduction and analysis cannot tolerate missing data: corrupted pixels must first have their values imputed. This paper presents astrofix, a robust and flexible image imputation algorithm based on Gaussian Process Regression (GPR). Through an optimization process, astrofix chooses and applies a different interpolation kernel to each image, using a training set extracted automatically from that image. It naturally handles clusters of bad pixels and image edges and adapts to various instruments and image types. For bright pixels, the mean absolute error of astrofix is several times smaller than that of median replacement and interpolation by a Gaussian kernel. We demonstrate good performance on both imaging and spectroscopic data, including the SBIG 6303 0.4m telescope and the FLOYDS spectrograph of Las Cumbres Observatory and the CHARIS integral-field spectrograph on the Subaru Telescope.