# DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1707.03386 · HTML: https://mlsanity.com/paper/1707.03386/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2017.

## Built on by (most cited first, 8 of 8)

- [CSformer: Bridging Convolution and Transformer for Compressive Sensing](https://mlsanity.com/paper/2112.15299/) (2021)
- [ADMM-DAD net: a deep unfolding network for analysis compressed sensing](https://mlsanity.com/paper/2110.06986/) (2021)
- [Unrolled Compressed Blind-Deconvolution](https://mlsanity.com/paper/2209.14165/) (2022)
- [Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems](https://mlsanity.com/paper/2103.00125/) (2021)
- [Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems](https://mlsanity.com/paper/2501.04608/) (2025)
- [DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees](https://mlsanity.com/paper/2101.05130/) (2021)
- [Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective](https://mlsanity.com/paper/2105.13191/) (2021)
- [Compressed Sensor Caching and Collaborative Sparse Data Recovery with Anchor Alignment](https://mlsanity.com/paper/2406.10137/) (2024)

## Abstract (arXiv)

In this paper we develop a novel computational sensing framework for sensing and recovering structured signals. When trained on a set of representative signals, our framework learns to take undersampled measurements and recover signals from them using a deep convolutional neural network. In other words, it learns a transformation from the original signals to a near-optimal number of undersampled measurements and the inverse transformation from measurements to signals. This is in contrast to traditional compressive sensing (CS) systems that use random linear measurements and convex optimization or iterative algorithms for signal recovery. We compare our new framework with ℓ1-minimization from the phase transition point of view and demonstrate that it outperforms ℓ1-minimization in the regions of phase transition plot where ℓ1-minimization cannot recover the exact solution. In addition, we experimentally demonstrate how learning measurements enhances the overall recovery performance, speeds up training of recovery framework, and leads to having fewer parameters to learn.