FSAPF: A De-Scattering Framework With Stepwise Adjustment of Polarization Features.

Deep learning has made significant advancements in polarization imaging through scattering media. However, there remains a deficiency in effective analysis and control of the abundant information embedded in polarization images during training. Therefore, we propose a de-scattering framework with stepwise adjustment of polarization features (FSAPF) for high-performance imaging through scattering media. This framework implements a physically guided hierarchical learning, in which supervision processes are progressively refined from global structure to local polarization details. To directly embed polarization priors into feature representations, a polarization learning module (PLM) is introduced, which regulates feature interactions by enforcing physical consistency constraints, thereby enabling the FSAPF to learn robust representations. In addition, by directly embedding polarization priors into the dynamic loss mechanism, polarization features can be enhanced during training, which further enhances the FSAPF's generalized robustness in changing scenarios. We conduct a series of validation experiments to verify the validity and superiority of the FSAPF. The experimental results show that the FSAPF can perform significantly in target recovery tasks under different scattering environments. And comparative and ablation experiments can both achieve better results. The code will be made public upon acceptance of the paper.