Compressive sensing inspired self-supervised single-pixel imaging

TL;DR

This paper introduces SISTA-Net, a self-supervised, compressive sensing-inspired deep learning method for single-pixel imaging that improves reconstruction quality and robustness in noisy environments.

Contribution

SISTA-Net uniquely combines a hybrid CNN-VSSM architecture with adaptive sparse transforms and a learnable threshold to enhance single-pixel image reconstruction.

Findings

Outperforms state-of-the-art methods by 2.6 dB in PSNR in simulations.

Achieves 3.4 dB average PSNR improvement in underwater tests.

Demonstrates robustness to interference at low sampling rates.

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 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.