# Quantized Compressed Sensing with Score-Based Generative Models

@article{Meng2022QuantizedCS, title={Quantized Compressed Sensing with Score-Based Generative Models}, author={Xiangming Meng and Yoshiyuki Kabashima}, journal={ArXiv}, year={2022}, volume={abs/2211.13006} }

We consider the general problem of recovering a high-dimensional signal from noisy quantized measurements. Quantization, especially coarse quantization such as 1-bit sign measurements, leads to severe information loss and thus a good prior knowledge of the unknown signal is helpful for accurate recovery. Motivated by the power of score-based generative models (SGM, also known as diffusion models) in capturing the rich structure of natural signals beyond simple sparsity, we propose an…

## 2 Citations

### Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems

- Computer ScienceArXiv
- 2022

An unsupervised general-purpose sampling approach called diffusion model based posterior sampling (DMPS) to reconstruct the unknown signal from noisy linear measurements and achieves highly competitive or even better performances on various tasks while being 3 times faster than the leading competitor.

### QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative Models for General Sensing Matrices

- Computer ScienceArXiv
- 2023

An improved version of QCS-SGM, which is based on a Bayesian inference perspective of the likelihood score computation, whereby an expectation propagation algorithm is proposed to approximately compute the likelihood scores, which works well for general matrices.

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