# Deep Learning Sparse Ternary Projections for Compressed Sensing of Images

arXiv: https://arxiv.org/abs/1708.08311 · HTML: https://mlsanity.com/paper/1708.08311/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [ATP-Net: An Attention-based Ternary Projection Network For Compressed Sensing](https://mlsanity.com/paper/2106.12728/) (2021)

## Abstract (arXiv)

Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix. The CS theory is based on random Gaussian projection matrices, which satisfy recovery guarantees with high probability; however, sparse ternary {0, -1, +1} projections are more suitable for hardware implementation. In this paper, we present a deep learning approach to obtain very sparse ternary projections for compressed sensing. Our deep learning architecture jointly learns a pair of a projection matrix and a reconstruction operator in an end-to-end fashion. The experimental results on real images demonstrate the effectiveness of the proposed approach compared to state-of-the-art methods, with significant advantage in terms of complexity.