Quantized Corrupted Sensing with Random Dithering

Corrupted sensing concerns the problem of recovering a high-dimensional structured signal from collection measurements that are contaminated by unknown corruption and unstructured noise. In case linear measurements, recovery performance different convex programming procedures (e.g., generalized Lasso its variants) is well established in literature. However, practical applications digital processing, quantization process inevitable, which often leads to non-linear measurements. This paper devoted studying corrupted under quantized Specifically, we demonstrate that, with aid uniform dithering, both constrained unconstrained Lassos able recover samples when measurement matrix sub-Gaussian. Our theoretical results reveal role resolution Lassos. Numerical experiments provided confirm our results.