A comparison of deep learning-based compressive imaging methods from a practitioner's perspective

In the past two decades, numerous Compressive Imaging (CI) techniques have been developed to reduce acquired data. Recently, these CI methods have incorporated Deep Learning (DL) tools to optimize both the reconstruction algorithm and the sensing model. However, most of these DL-based CI methods have been developed by simulating the sensing process without considering the limitations associated with the optical realization of the optimized sensing model. Since the merit of CI stands with the physical realization of the sensing process, we revisit the leading DL-based CI methods. We present a preliminary comparison of their performances while focusing on practical aspects such as the realizability of the sensing matrix and robustness to the measurement noise.