Imaging through scattering media via generative diffusion model (JCR-Q2, Third author)
Published in Applied Physics Letters, 2024
Abstract
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.
Key words
Image processing algorithms, Deep learning, Optical imaging, Optical scattering
Recommended citation: Chen ZY, Lin BY, Gao SY, Wan WB, Liu QG. Imaging through scattering media via generative diffusion model.Appl. Phys. Lett., 2024, 124: 051101.
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