Optical diffractive neural network imaging through double diffusive mediums

Abstract

The optical diffractive neural network (ODNN) offers the benefits of high-speed parallelism and low energy consumption. This kind of method holds great potential in the task of reconstructing diffusive images. In this work, we capture a double-scattering dataset by designing optical experiments and use it to evaluate the image reconstruction capability of the constructed ODNNs under more complex scattering scenarios. The Pearson Correlation Coefficient, which is used as a quantitative index of the reconstruction performance, shows that the constructed diffractive networks enable to achieve high performance in the direct recovery of double-scattering data, as well as in the recovery task of stitching images based on two different kinds of double-scattering data. Meanwhile, due to the high redundancy of valid information in the speckle patterns of scattering images, even if parts of the information in the speckle patterns are blocked, the constructed diffractive networks can also show high reconstruction performance without retraining. The capability of the proposed ODNN to reconstruct double-scattering images indicates that the optical diffractive network has the potential to bring transformative applications in more complex scattering scenarios.