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The non-negativity of the measurement matrix in the traditional compressed ghost imaging system and iterative optimization process leads to low imaging quality and slow reconstruction speed. This paper proposes a singular value decomposition compressed ghost imaging method based on deep unfolding. The measurement matrix and training data pairs are generated through numerical simulation to reduces the cost of data acquisition. This paper adds a preprocessing layer to the network, which performs singular value decomposition on the measurement matrix to simultaneously obtain an optimized semi-orthogonal measurement matrix and optimized measurements. Then, iterative shrinking threshold algorithm network (ISTA-Net +) is used to learn the mapping between the measurements to the original signal from the training data set. Finally, the trained deep neural network can achieve non-iterative real-time reconstruction of high-quality images from low sampling rate measurements. Numerical experiments demonstrate that our proposed method has good reconstruction performance and good anti-noise performance at low sampling rates. [ABSTRACT FROM AUTHOR]

Compressed ghost imaging method can effectively reduce the number of measurements required for ghost imaging reconstruction. The non-negative characteristics of measurement matrix in compressed ghost imaging method is inconsistent with the requirements of the measurement matrix in traditional compressed sensing theory, leading to low quality reconstruction. Aiming at the point, this paper proposes a singular value decomposition compressed ghost imaging method to improve the reconstruction quality of ghost imaging. First, the singular value decomposition is performed on the measurement matrix, and then the optimized measurement matrix and measurements are obtained, finally the reconstruction of the image is completed by the reconstruction algorithm. Numerical simulation experiments verify the superiority of our proposed singular value decomposition compressed ghost imaging method. [ABSTRACT FROM AUTHOR]