Imaging through scattering media via generative diffusion model

The scattering medium scrambles the light paths emitted from the targets into speckle patterns, leading to a significant degradation of the target image. Conventional iterative phase recovery algorithms typically yield low-quality reconstructions. On the other hand, supervised learning methods exhibit limited generalization capabilities in the context of image reconstruction. An approach is proposed for achieving high-quality reconstructed target images through scattering media using a diffusion generative model. The gradient distribution prior information of the target image is modeled using a scoring function, which is then utilized to constrain the iterative reconstruction process. The high-quality target image is generated by alternatively performing the stochastic differential equation solver and physical model-based data consistency steps. Simulation and experimental validation demonstrate that the proposed method achieves better image reconstruction quality compared to traditional methods, while ensuring generalization capabilities.