A Deep Learning Approach to Structured Signal Recovery

Abstract

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.

Cited by in corpus (24)

• AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
• AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing
• COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing
• RARE: Image Reconstruction using Deep Priors Learned without Ground Truth
• Distributed Compressive Sensing: A Deep Learning Approach
• Content-aware Scalable Deep Compressed Sensing
• Memory-Augmented Deep Unfolding Network for Compressive Sensing
• Image Compressed Sensing Using Non-local Neural Network
• FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements
• Learning optimal nonlinearities for iterative thresholding algorithms
• Deep Learning for Passive Synthetic Aperture Radar
• Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging
• SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors
• AutoBCS: Block-based Image Compressive Sensing with Data-driven Acquisition and Non-iterative Reconstruction
• Compressive Spectral Image Reconstruction using Deep Prior and Low-Rank Tensor Representation
• Limitations of Deep Learning for Inverse Problems on Digital Hardware
• Adaptive Block Compressive Sensing: towards a real-time and low-complexity implementation
• Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines
• Perceptual Compressive Sensing
• Uformer-ICS: A U-Shaped Transformer for Image Compressive Sensing Service
• Periodic Astrometric Signal Recovery through Convolutional Autoencoders
• Frequency Estimation in Data Streams: Learning the Optimal Hashing Scheme
• Minimizing Acquisition Maximizing Inference -- A demonstration on print error detection
• JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imaging