Physics-Inspired Compressive Sensing: Beyond deep unrolling

As an emerging paradigm for signal acquisition and reconstruction, compressive sensing (CS) achieves high-speed sampling and compression jointly and has found its way into many applications. With the fast growth of deep learning in computer vision, various methods of applying neural networks (NNs) in CS imaging tasks have been proposed. One category of them, named the deep unrolling network, is inspired by the physical sampling model and combines the merits of both optimization model- and data-driven methods, becoming the mainstream of this realm. In this review article, we first review the inverse imaging model and optimization algorithms encountered in the CS research and then provide the recent representative developments of CS networks, which are grouped into deep physics-free and physics-inspired approaches with respect to the utilization of sampling matrix and measurement information. Following this, we analyze the conceptual connections and relationships among various existing methods and present our perspectives on recent advances and trends for future research.