Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging
This work addresses the challenge of scarce paired training data for denoising in astronomical imaging, which is crucial for scientific workflows, though it is incremental as it builds on existing learning-based methods.
The paper tackled the problem of noise-limited astronomical imaging by proposing a physics-based noise synthesis framework to generate abundant paired training data for learning-based denoising, resulting in demonstrated effectiveness in photometric and scientific accuracy on real-world multi-band datasets.
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.