A Deep Learning Approach to Structured Signal Recovery

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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.