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

Astronomical imaging remains noise-limited under practicalobserving conditions. Standard calibration pipelines remove structuredartifacts but largely leave stochastic noise unresolved. Although learning-based denoising has shown strong potential, progress is constrained byscarce paired training data and the requirement for physically inter-pretable models in scientific workflows. We propose a physics-based noisesynthesis 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 fromcosmic-ray hits and hot pixels. To obtain low-noise inputs for synthesis,we stack multiple unregistered exposures to produce high-SNR bases. Re-alistic noisy counterparts synthesized from these bases using our noisemodel enable the construction of abundant paired datasets for supervisedlearning. Extensive experiments on our real-world multi-band datasetcurated from two ground-based telescopes demonstrate the effectivenessof our framework in both photometric and scientific accuracy. Code isavailable at https://github.com/ShuhongLL/Denoising-Deep-Sky.