mlsanityData to 14 Oct 2026

A Deep Learning Approach to Structured Signal Recovery

arXiv 1508.04065 ↗First posted 17 Aug 2015cs.LG stat.MLcited by 43

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Abstract (arXiv)

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.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Aug 2015.

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Citing papers per year: 2021: 16; 2022: 9; 2023: 7; 2024: 6; 2025: 2; 2026: 3.

43 papers.

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  1. 2022Generative Joint Source-Channel Coding for Semantic Image Transmissioncited by 58
  2. 2021Memory-Augmented Deep Unfolding Network for Compressive Sensingcited by 38
  3. 2021COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensingcited by 32
  4. 2021Learning-Augmented k-means Clusteringcited by 26
  5. 2021Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Conditioncited by 22
  6. 2022Content-aware Scalable Deep Compressed Sensingcited by 21
  7. 2023Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image Reconstructioncited by 20
  8. 2021Learning-based Support Estimation in Sublinear Timecited by 16
  9. 2021Analysis of feature learning in weight-tied autoencoders via the mean field lenscited by 14
  10. 2024Invertible Diffusion Models for Compressed Sensingcited by 12
  11. 2021Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networkscited by 11
  12. 2021Image Compressed Sensing Using Non-local Neural Networkcited by 9
31 more
  1. 2022Limitations of Deep Learning for Inverse Problems on Digital Hardwarecited by 9
  2. 2023Self-Supervised Scalable Deep Compressed Sensingcited by 8
  3. 2024Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learningcited by 8
  4. 2022Global Sensing and Measurements Reuse for Image Compressed Sensingcited by 7
  5. 2023Deep Physics-Guided Unrolling Generalization for Compressed Sensingcited by 7
  6. 2025Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Modelscited by 7
  7. 2022JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imagingcited by 5
  8. 2023Deep Unfolding Network for Image Compressed Sensing by Content-adaptive Gradient Updating and Deformation-invariant Non-local Modelingcited by 5
  9. 2021Dynamic Proximal Unrolling Network for Compressive Imagingcited by 4
  10. 2021Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operationcited by 4
  11. 2025Factor Informed Double Deep Learning For Average Treatment Effect Estimationcited by 2
  12. 2021Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systemscited by 1
  13. 2021Provably Convergent Algorithms for Solving Inverse Problems Using Generative Modelscited by 1
  14. 2022Uformer-ICS: A U-Shaped Transformer for Image Compressive Sensing Servicecited by 1
  15. 2023Recursions Are All You Need: Towards Efficient Deep Unfolding Networkscited by 1
  16. 2024Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overviewcited by 1
  17. 2024Deep Network for Image Compressed Sensing Coding Using Local Structural Samplingcited by 1
  18. 2021Scalable Deep Compressive Sensingcited by 0
  19. 2021Quantized Proximal Averaging Network for Analysis Sparse Codingcited by 0
  20. 2021Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspectivecited by 0
  21. 2021ATP-Net: An Attention-based Ternary Projection Network For Compressed Sensingcited by 0
  22. 2022Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learningcited by 0
  23. 2022Image Compressed Sensing with Multi-scale Dilated Convolutional Neural Networkcited by 0
  24. 2022JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensingcited by 0
  25. 2023A Lightweight Recurrent Learning Network for Sustainable Compressed Sensingcited by 0
  26. 2023NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensingcited by 0
  27. 2024Compressed Sensor Caching and Collaborative Sparse Data Recovery with Anchor Alignmentcited by 0
  28. 2024WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensingcited by 0
  29. 2026Minimax Theory of Likelihood-Based Deep Learning for Speckle Regressioncited by 0
  30. 2026Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matricescited by 0
  31. 2026PE-CSNet: An equivariant network architecture with learnable patch-based sparse representationcited by 0