QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative Models

• URL: http://arxiv.org/abs/2302.00919v4
• Date: Mon, 8 Jan 2024 13:19:38 GMT
• Title: QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative Models
• Authors: Xiangming Meng and Yoshiyuki Kabashima
• Abstract summary: In practical compressed sensing (CS), the obtained measurements typically necessitate quantization to a limited number of bits prior to transmission or storage. We introduce an advanced variant of QCS-SGM, termed QCS-SGM+, capable of handling general matrices effectively.
• Score: 17.49551570305112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In practical compressed sensing (CS), the obtained measurements typically necessitate quantization to a limited number of bits prior to transmission or storage. This nonlinear quantization process poses significant recovery challenges, particularly with extreme coarse quantization such as 1-bit. Recently, an efficient algorithm called QCS-SGM was proposed for quantized CS (QCS) which utilizes score-based generative models (SGM) as an implicit prior. Due to the adeptness of SGM in capturing the intricate structures of natural signals, QCS-SGM substantially outperforms previous QCS methods. However, QCS-SGM is constrained to (approximately) row-orthogonal sensing matrices as the computation of the likelihood score becomes intractable otherwise. To address this limitation, we introduce an advanced variant of QCS-SGM, termed QCS-SGM+, capable of handling general matrices effectively. The key idea is a Bayesian inference perspective on the likelihood score computation, wherein expectation propagation is employed for its approximate computation. Extensive experiments are conducted, demonstrating the substantial superiority of QCS-SGM+ over QCS-SGM for general sensing matrices beyond mere row-orthogonality.

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