Acquisition-inspired motion artifact modeling and degradation-consistent diffusion restoration in photoacoustic microscopy

Motion artifacts are a major source of degradation in photoacoustic microscopy (PAM), where sequential raster scanning converts target-probe motion into structured coordinate mismatch, causing line misregistration, vessel discontinuity, truncation, and local deformation. We propose an acquisition-inspired framework for PAM motion artifact modeling and degradation-guided diffusion restoration. Motion corruption is formulated as raster-scan coordinate mismatch and decomposed into inter-line misregistration and intra-line sampling distortion. Based on this formulation, we construct a controllable simulator that generates paired clean-corrupted PAM images with explicit degradation priors. A conditional diffusion restoration model then uses motion-related priors for guidance and enforces degradation consistency by applying the forward inter-line and intra-line degradation operators during training. To support real-data inference, a degradation prior estimator predicts row-wise displacement and artifact-region information from corrupted images. Experiments on synthetic mouse brain PAM data show superior restoration performance, while selected real examples provide preliminary qualitative observations on non-synthetic motion-corrupted acquisitions.