Diffusion Model Based Signal Recovery Under 1-Bit Quantization

Youming Chen; Zhaoqiang Liu

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arXiv · 2511.12471

Diffusion Model Based Signal Recovery Under 1-Bit Quantization

Youming Chen·Zhaoqiang Liu

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Abstract

Diffusion models (DMs) have demonstrated to be powerful priors for signal recovery, but their application to 1-bit quantization tasks, such as 1-bit compressed sensing and logistic regression, remains a challenge. This difficulty stems from the inherent non-linear link function in these tasks, which is either non-differentiable or lacks an explicit characterization. To tackle this issue, we introduce Diff-OneBit, which is a fast and effective DM-based approach for signal recovery under 1-bit quantization. Diff-OneBit addresses the challenge posed by non-differentiable or implicit links functions via leveraging a differentiable surrogate likelihood function to model 1-bit quantization, thereby enabling gradient based iterations. This function is integrated into a flexible plug-and-play framework that decouples the data-fidelity term from the diffusion prior, allowing any pretrained DM to act as a denoiser within the iterative reconstruction process. Extensive experiments on the FFHQ, CelebA and ImageNet datasets demonstrate that Diff-OneBit gives high-fidelity reconstructed images, outperforming state-of-the-art methods in both reconstruction quality and computational efficiency across 1-bit compressed sensing and logistic regression tasks. Our code is available at https://github.com/Chenyouming123/DiffOneBit.

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BibTeX RIS

Youming Chen, Zhaoqiang Liu. 2026-01-10. Diffusion Model Based Signal Recovery Under 1-Bit Quantization. https://arxiv.org/abs/2511.12471

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