A Deep Learning Approach to Structured Signal Recovery
This addresses signal recovery for applications like imaging or communications, but it is incremental as it adapts existing deep learning techniques to a known bottleneck.
The paper tackles the problem of structured signal recovery by introducing a deep learning framework that uses stacked denoising autoencoders to learn representations from data, improving performance over compressive sensing methods.
In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach.