DSCR-Net: A Diffractive Sensing and Complex-Valued Reconstruction Network for Compressive Sensing

Recently, deep learning based compressive sensing (CS) methods show superior reconstruction performance. However, these approaches are restricted for compressive video sampling in practical design of imaging devices. In this paper, we propose a novel deep network-based CS framework for efficient optical implementation. The proposed framework, dubbed DSCR-Net, consists of a diffractive sensing network employing light diffraction for efficient sampling and a complex-valued neural network for reconstruction, respectively. The diffractive sensing network can achieve real-time sampling at the speed of light. Furthermore, complex-valued neural network is developed to facilitate the reconstruction quality from the complex-valued measurements by jointly considering their real and imaginary parts. Extensive experiments demonstrate that DSCR-Net outperforms the state-of-the-art CS methods in the low sampling-rate region with a potential of immediate implementation of imaging device.