Title: Latent Space Single-Pixel Imaging Under Low-Sampling Conditions Show Chinese title

Title: 低采样条件下的潜在空间单像素成像

Abstract: In recent years, the introduction of deep learning into the field of single-pixel imaging has garnered significant attention. However, traditional networks often operate within the pixel space. To address this, we innovatively migrate single-pixel imaging to the latent space, naming this framework LSSPI (Latent Space Single-Pixel Imaging). Within the latent space, we conduct in-depth explorations into both reconstruction and generation tasks for single-pixel imaging. Notably, this approach significantly enhances imaging capabilities even under low sampling rate conditions. Compared to conventional deep learning networks, LSSPI not only reconstructs images with higher signal-to-noise ratios (SNR) and richer details under equivalent sampling rates but also enables blind denoising and effective recovery of high-frequency information. Furthermore, by migrating single-pixel imaging to the latent space, LSSPI achieves superior advantages in terms of model parameter efficiency and reconstruction speed. Its excellent computational efficiency further positions it as an ideal solution for low-sampling single-pixel imaging applications, effectively driving the practical implementation of single-pixel imaging technology. Show Chinese abstract

Abstract: 近年来，将深度学习引入单像素成像领域引起了广泛关注。 然而，传统网络通常在像素空间中运行。 为了解决这个问题，我们创新性地将单像素成像迁移到潜在空间，将这个框架命名为LSSPI（潜在空间单像素成像）。 在潜在空间中，我们对单像素成像的重建和生成任务进行了深入探索。 值得注意的是，即使在低采样率条件下，这种方法也能显著提升成像能力。 与传统的深度学习网络相比，LSSPI不仅在同等采样率下能够以更高的信噪比（SNR）和更丰富的细节重建图像，还能够实现盲去噪和高频信息的有效恢复。 此外，通过将单像素成像迁移到潜在空间，LSSPI在模型参数效率和重建速度方面具有显著优势。 其出色的计算效率进一步使其成为低采样单像素成像应用的理想解决方案，有效推动了单像素成像技术的实际应用。