用压缩感知启发的自监督方法提升单像素成像质量
Compressive sensing inspired self-supervised single-pixel imaging
- 将迭代收缩阈值算法展开为可解释网络，融合局部与全局特征建模
- 在极低采样率下仍保持高保真，模拟实验提升2.6 dB PSNR，水下实测平均提升3.4 dB
- 适合强干扰环境下的单像素成像，尤其适用于水下远场等挑战场景
单像素成像（SPI）在强扰动环境中具有显著优势。现有方法缺乏物理稀疏性约束，且忽略局部与全局特征的融合，导致噪声敏感、结构失真和细节模糊。为此，我们提出SISTA-Net，一种受压缩感知启发的自监督单像素成像方法。该方法将迭代收缩阈值算法（ISTA）展开为可解释网络，包含数据保真模块和近端映射模块。保真模块采用混合CNN-视觉状态空间模型（VSSM）架构，融合局部与全局特征，增强重建完整性和保真度。通过深度非线性网络作为自适应稀疏变换，并结合可学习软阈值算子，在隐空间显式引入物理稀疏性，实现噪声抑制与抗干扰能力，即使在极低采样率下也表现稳健。多个仿真场景的大量实验表明，SISTA-Net在PSNR上优于当前最优方法2.6 dB；真实世界远场水下测试中，平均PSNR提升达3.4 dB，验证了其强大的抗干扰能力。
原文摘要 · Abstract (English)
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 features, leading to severe noise vulnerability, structural distortions and blurred details. To address these limitations, we propose SISTA-Net, a compressive sensing-inspired self-supervised method for single-pixel imaging. SISTA-Net unfolds the Iterative Shrinkage-Thresholding Algorithm (ISTA) into an interpretable network consisting of a data fidelity module and a proximal mapping module. The fidelity module adopts a hybrid CNN-Visual State Space Model (VSSM) architecture to integrate local and global feature modeling, enhancing reconstruction integrity and fidelity. We leverage deep nonlinear networks as adaptive sparse transforms combined with a learnable soft-thresholding operator to impose explicit physical sparsity in the latent domain, enabling noise suppression and robustness to interference even at extremely low sampling rates. Extensive experiments on multiple simulation scenarios demonstrate that SISTA-Net outperforms state-of-the-art methods by 2.6 dB in PSNR. Real-world far-field underwater tests yield a 3.4 dB average PSNR improvement, validating its robust anti-interference capability.
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