The Ghost Imaging Method Based on Dual Attention Mechanism and Orthogonal Optimization

Abstract Ghost imaging (GI) has shown potential in low-light and complex scenes, but its performance is limited by noise and low-quality reconstruction under extreme sampling rates. This paper proposes a deep learning method combining dual attention mechanism with orthogonal regularization, called Dual Attention Orthogonal Optimization Ghost Imaging (DAOGI). The method uses the AttentionBlock module to enhance local feature extraction and the Transformer module to capture global dependencies. Meanwhile, the orthogonal regularization term is employed to strengthen the orthogonality of the modulation pattern matrix, thereby improving imaging quality at extremely low sampling rates and effectively reducing artifacts and noise. The DAOGI method can significantly outperform traditional ghost imaging methods at a sampling rate of 1.56$\%$, achieving high-quality image reconstruction with few samples. The feasibility and effectiveness of the proposed method are verified through numerical simulations and optical experiments, providing a new direction for ghost imaging.