Recently, deep learning has been tried to improve the efficiency of compressed ghost imaging. However, these current learning-based ghost imaging methods have to modify and retrain the learning model to cope with different sampling ratios. This will consume a lot of computing resources and energy. In this paper, we propose a deep learning-based compressed ghost imaging method that can adapt to arbitrary sampling ratios without tailoring and retraining model. By simultaneously optimizing the weights of both the speckle patterns and the transformer model, we achieve a network for ghost imaging at arbitrary sampling ratios. The feasibility and effectiveness of the proposed method were validated through numerical simulations. The results indicate that the proposed method, requiring only a single training session, is capable of reconstructing high-quality images under varying sampling ratios. Furthermore, the performance of the proposed method surpasses that of currently widely employed deep learning ghost imaging methods. At a sampling ratio of 5%, the proposed method achieves an increase of 1.87 dB in Peak Signal-to-Noise Ratio (PSNR) and 0.171 in Structural Similarity Index (SSIM).

中文翻译：

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基于 Transformer 的灵活采样比压缩鬼影


最近，人们尝试使用深度学习来提高压缩鬼成像的效率。然而，目前这些基于学习的鬼影成像方法必须修改和重新训练学习模型，以应对不同的采样率。这将消耗大量的计算资源和能源。在本文中，我们提出了一种基于深度学习的压缩幽灵成像方法，该方法可以适应任意采样率，而无需定制和重新训练模型。通过同时优化散斑图案和 transformer 模型的权重，我们实现了任意采样率的虚影成像网络。通过数值模拟验证了所提方法的可行性和有效性。结果表明，所提出的方法只需要一次训练，能够在不同的采样率下重建高质量的图像。此外，所提出的方法的性能超过了目前广泛采用的深度学习幽灵成像方法。在 5% 的采样率下，所提出的方法实现了峰值信噪比 （PSNR） 增加 1.87 dB 和结构相似性指数 （SSIM） 0.171 dB 的增加。