Physics-Preserving Attention-Guided Artifact Removal for Spaceborne Optical Images

Abstract

Artifacts, including halos and saturated bright spots, are common in spaceborne optical images and can degrade the reliability of star extraction, space object detection, and photometric analysis. Traditional signal-processing methods, such as morphological filtering and low-rank decomposition, rely on fixed priors that may fail under complex artifact morphologies. Deep restoration networks can improve visual quality but do not explicitly enforce radiometric consistency. Multimodal instruction-driven editing models provide semantic localization capability, but probabilistic diffusion resampling can introduce uncontrolled pixel changes in non-target regions, compromising pixel-level physical consistency. We refer to this problem as editing-induced radiometric drift. To address this problem, we propose PARE, a physics-preserving attention-guided artifact removal framework for spaceborne optical images. Instead of directly using the edited image as the final restoration, PARE treats it as a candidate restoration and derives artifact-region constraints from the image-to-text cross-attention sub-block of the MM-DiT joint attention matrix. These constraints are combined with multi-scale fusion to restrict generative modification to localized artifact regions, thereby enabling artifact suppression while reducing unintended changes in non-target regions. Experiments on simulated and real spaceborne optical image datasets show that PARE achieves effective artifact suppression while improving radiometric preservation. On real on-orbit images, PARE reaches 92.95% artifact mean reduction (AMR) and 98.57% artifact energy reduction (AER), reduces the outer-region mean absolute error (O-MAE) to 0.54, and improves the outer-region structural similarity index (O-SSIM) to 0.997. It also yields the smallest background shifts among all compared methods. These results indicate that PARE provides a favorable trade-off between artifact suppression and radiometric fidelity and offers a practical way to apply generative models to scientific imaging tasks that require pixel-level physical consistency.