Technical Program

Paper Detail

Paper IDG.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.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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