2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | G.3.3 | ||
| Paper Title | Quantized Corrupted Sensing with Random Dithering | ||
| Authors | Zhongxing Sun, Wei Cui, Yulong Liu, Beijing Institute of Technology, China | ||
| Session | G.3: Compressed Sensing | ||
| Presentation | Lecture | ||
| Track | Graphs, Games, Sparsity, and Signal Processing | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | Quantized corrupted sensing concerns the problem of estimating structured signals from their quantized corrupted samples. A typical case is that when the measurements $\bm{y}=\bm{\Phi}{\bm{x}}^{\star}+{\bm{v}}^{\star}+\bm{n}$ are corrupted with both structured corruption ${\bm{v}}^{\star}$ and unstructured noise $\bm{n}$, we wish to reconstruct ${\bm{x}}^{\star}$ and ${\bm{v}}^{\star}$ from the quantized samples of $\bm{y}$. Our work shows that the Generalized Lasso can be applied for the recovery of signal provided that a uniform random dithering is added to the measurements before quantization. The theoretical results illustrate that the influence of quantization behaves as independent unstructured noise. We also confirm our results numerically in several scenarios such as sparse vectors and low-rank matrices. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
