A hybrid optronic neural network for image classification through scattering media

Image classification behind complex inhomogeneous media is a pervasive problem in computational optics. In recent years, optical neural networks have shown high accuracy and little computation costs in image classification. However, the improvements in scalability and complexity are still challenging. This paper presents an optronic speckle transformer (OPST) for image classification through scattering media. We utilize the optical self-attention mechanism to extract the speckle pattern's local and global properties. We realize excellent speckle classification results with minimal computation costs based on OPST. The OPST improves the classification by more than 8% and reduces the network's parameter by more than 30%, compared with optronic convolutional neural networks (OPCNN). Moreover, our OPST demonstrates high scalability with existing optical neural networks and is adaptive to more complex tasks. Our work paves the way to an all-optical approach with less computational costs for object classification through opaque media.