Reviews: DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging

Traditional acoustic camera methods had been advanced significantly with the advent of compressed sensing techniques, which reconstruct the original signals successfully by means of hand-crafted features or functions with nonlinear optimization, e.g., proximal gradient descent. However, the performance of the reconstruction process has been significantly slow due to nonlinear optimization steps. This paper proposes a new approach that substitutes the traditional nonlinear optimization approach with recurrent network architecture, i.e., by unrolling the iterative convex optimization algorithm in a form of neural network architecture. This paper takes a two-layered design, where a bias and back-projection gradient, and deblurring matrix are learned. As described in the paper, the recurrent architecture has been proposed to substitute the signal reconstruction problem for other field applications of compressive sensings, such as compressive imaging.