English(EN) Compressive sensing inspired self-supervised single-pixel imaging

新的SISTA-Net方法利用压缩感知增强单像素成像

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了SISTA-Net，一种受压缩感知原理启发的、用于单像素成像的新型自监督方法。该方法通过结合物理稀疏性约束并整合局部和全局特征来解决现有方法的局限性。SISTA-Net采用混合CNN-Visual State Space Model架构进行特征建模，并采用具有可学习软阈值算子的自适应稀疏变换来增强降噪和鲁棒性，即使在低采样率下也是如此。实验结果表明，SISTA-Net的性能优于当前最先进的方法，在模拟和真实的现实世界水下测试中均取得了显著的PSNR改进。 AI

影响这项新的成像技术有望在挑战性环境和低数据场景下提高性能。

排序理由这是一篇详细介绍一种新的单像素成像方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Jijun Lu, Yifan Chen, Libang Chen, Yiqiang Zhou, Ye Zheng, Mingliang Chen, Zhe Sun, Xuelong Li · 2026-09-17 04:00

受压缩感知启发的自监督单像素成像

arXiv:2603.29732v2 Announce Type: replace Abstract: Single-pixel imaging (SPI) is a promising imaging modality with distinctive advantages in strongly perturbed environments. Existing SPI methods lack physical sparsity constraints and overlook the integration of local and global …

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受压缩感知启发的自监督单像素成像

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