Non-invasive imaging through scattering medium and around corners beyond 3D memory effect

The three-dimensional (3D) memory effect (ME) has been shown to exist in a variety of scattering scenes. Limited by the scope of ME, speckle correlation technology only can be applied in a small imaging field of view (FOV) with a small depth of field (DOF). In this Letter, an untrained neural network is constructed and used as an optimization tool to restore the targets beyond the 3D ME range. The autocorrelation consistency relationship and the generative adversarial strategy are combined. Only single frame speckle and unaligned real targets are needed for online optimization; therefore, the neural network does not need to train in advance. Furthermore, the proposed method does not need to conduct additional modulation for the system. This method can reconstruct not only hidden targets behind the scattering medium, but also targets around corners. The combination strategy of the generative adversarial framework with physical priors used to decouple the aliasing information and reconstruct the target will provide inspiration for the field of computational imaging. (C) 2022 Optica Publishing Group

Automated summary

In this study, an untrained neural network is proposed as an optimization tool to restore targets beyond the range of three-dimensional memory effect (3D ME). By combining the autocorrelation consistency relationship and the generative adversarial strategy, online optimization can be achieved with only single frame speckle and unaligned real targets.