High-quality ghost imaging based on undersampled natural-order Hadamard source

Abstract

Abstract Improving the speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical applications. Because of the proportional relationship between image resolution and measurement time, when the image pixels are large, the measurement time increases, making it difficult to achieve real-time imaging. Therefore, a high-quality ghost imaging method based on undersampled natural-order Hadamard is proposed. This method uses the characteristics of the Hadamard matrix under undersampling conditions where image information can be fully obtained but overlaps, as well as deep learning to extract aliasing information from the overlapping results to obtain the true original image information. We conducted numerical simulations and experimental tests on binary and grayscale objects under undersampling conditions to demonstrate the effectiveness and scalability of this method. This method can significantly reduce the number of measurements required to obtain high-quality image information and advance application promotion.