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

Improving the imaging speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical applications. Due to the proportional relationship between image resolution and measurement times, when the image pixels are large, the measurement times also increase, making it difficult to achieve real-time imaging. Therefore, a high-quality ghost imaging method based on undersampling natural order Hadamard is proposed. This method utilizes the characteristics of the Hadamard matrix under undersampling conditions where image information can be fully obtained but overlaps, and utilizes deep learning to extract the 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, demonstrating the effectiveness and scalability of this method. This method can significantly reduce the number of measurements required for obtaining high-quality image information and promote application promotion.