TurboMPLE: Joint Infrared Turbulence Mitigation and Physical Fields Estimation via Mutual Progressive Layered Extraction

Atmospheric turbulence severely degrades thermal infraredimaging quality, yet most existing mitigation methods focus solely on im-age restoration while ignoring underlying physical characteristics. In thisstudy, we construct a large-scale infrared turbulence imaging dataset andproposed the Turbulence-oriented Mutual Progressive Layered Extrac-tion (TurboMPLE), a joint network for infrared turbulence mitigationand physical fields estimation. Through Mutual PLE Blocks, TurboM-PLE achieves progressive feature-level collaboration, introducing turbu-lence priors into the mitigation process while incorporating richer visualinformation into physical fields estimation. In addition, embedding theLambert–Beer law and turbulent physics constraints enables more in-terpretable results. With a lightweight and computationally efficient de-sign, TurboMPLE achieves state-of-the-art performance on both tasksand demonstrates strong generalization capability on real-world data.The code is available at https://github.com/Ayt777/TurboMPLE.