Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging

TL;DR

This paper introduces a physics-based noise synthesis framework for astronomical CCD imaging that enables the creation of paired datasets for supervised denoising, improving scientific accuracy in noisy astronomical images.

Contribution

We develop a comprehensive physics-based noise model for CCDs that facilitates realistic noise synthesis and supports supervised learning for astronomical image denoising.

Findings

Synthesized datasets improve denoising performance.

Framework enhances photometric accuracy in noisy images.

Effective across multi-band astronomical datasets.

Abstract

Astronomical imaging remains noise-limited under practical observing conditions. Standard calibration pipelines remove structured artifacts but largely leave stochastic noise unresolved. Although learning-based denoising has shown strong potential, progress is constrained by scarce paired training data and the requirement for physically interpretable models in scientific workflows. We propose a physics-based noise synthesis framework tailored to CCD noise formation in the telescope. The pipeline models photon shot noise, photo-response non-uniformity, dark-current noise, readout effects, and localized outliers arising from cosmic-ray hits and hot pixels. To obtain low-noise inputs for synthesis, we stack multiple unregistered exposures to produce high-SNR bases. Realistic noisy counterparts synthesized from these bases using our noise model enable the construction of abundant paired datasets for supervised learning. Extensive experiments on our real-world multi-band dataset curated from two ground-based telescopes demonstrate the effectiveness of our framework in both photometric and scientific accuracy.