Bridging optics and machine learning: revisiting correspondence imaging via linear classification

We analyze correspondence ghost imaging (CGI) from a machine learning perspective, focusing on its intrinsic mathematical consistency with the perceptron model. Based on this analysis, linear classification algorithms such as the perceptron and logistic regression are applied to CGI data, yielding improved image quality compared to traditional methods. Experimental and simulation results demonstrate that logistic regression achieves the highest structural similarity index (SSIM) in reconstruction tasks. Furthermore, CGI is employed for solving standard classification tasks, achieving comparable accuracy with dramatically lower computation time-up to 10× faster than logistic regression. These findings uncover the potential of interpreting CGI as a lightweight linear classifier.