Multi-channel computational ghost imaging based on multi-scale speckle optimization

Abstract A multi-channel computational ghost imaging (GI) method based on multi-scale speckle optimization is proposed. We not only reduce imaging time and enhance imaging quality but also reduce interference among different channels. Using one bucket detector to receive total light intensity, the color speckle is formed by combining components obtained through the singular value decomposition of three self-designed multi-scale measurement matrices. Simulation and experimental results demonstrate that our designed method contributes to reducing imaging time and enhancing imaging quality, achieving improved visual quality even at low sampling rates. This approach enhances GI flexibility and holds potential for diverse applications, including target recognition and biomedical imaging.