Abstract

We present an end-to-end deep learning model designed to reconstruct up to eight frames from a single snapshot of a dynamic object passing through an unknown, time-varying scattering medium. Our approach integrates a coded aperture compressive temporal imaging system with a specially designed transformer-based convolutional neural network (CNN), optimized for effective demultiplexing and reconstruction. Both simulation and experimental results demonstrate a successful compression ratio of up to 8X, while maintaining high reconstruction quality. Furthermore, ablation studies reveal that our dual-input CNN model, which utilizes both speckle patterns and their autocor relations, significantly improves reconstruction accuracy. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement