A Lightweight Conditional Diffusion Model for Restoring Turbulence-degraded Facial Images

Atmospheric turbulence induces severe blurring and geometric distortions in facial imagery, critically compromising the performance of downstream tasks.To overcome this challenge, a lightweight conditional diffusion model was proposed for the restoration of single-frame turbulence-degraded facial images.Superresolution techniques were integrated with the diffusion model, and high-frequency information was incorporated as a conditional constraint to enhance structural recovery and achieve high-fidelity generation.A simplified U-Net architecture was employed within the diffusion model to reduce computational complexity while maintaining high restoration quality.Comprehensive comparative evaluations and restoration experiments across multiple scenarios demonstrate that the proposed method produces results with reduced perceptual and distributional discrepancies from ground-truth images, while also exhibiting superior inference efficiency compared to existing approaches.The presented approach not only offers a practical solution for enhancing facial imagery in turbulent environments but also establishes a promising paradigm for applying efficient diffusion models to ill-posed image restoration problems, with potential applicability to other domains such as medical and astronomical imaging.