Transformer-based flexible sampling ratio compressed ghost imaging

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).