Compressive sensing inspired self-supervised single-pixel imaging
This work addresses imaging challenges in perturbed environments like underwater settings, offering a robust solution with concrete performance gains, though it appears incremental as it builds on existing compressive sensing and deep learning techniques.
The paper tackled the problem of noise vulnerability and structural distortions in single-pixel imaging by proposing SISTA-Net, a compressive sensing-inspired self-supervised method, which outperformed state-of-the-art methods by 2.6 dB in PSNR in simulations and achieved a 3.4 dB average PSNR improvement in real-world underwater tests.
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.