Abstract
High-quality reconstruction under a low sampling rate is very important for ghost imaging. How to obtain perfect imaging results from the low sampling rate has become a research hotspot in ghost imaging. In this paper, inspired by matrix optimization in compressed sensing, an optimization scheme of speckle patterns via measurement-driven framework is introduced to improve the reconstruction quality of ghost imaging. According to this framework, the sampling matrix and sparse basis are optimized alternately using the sparse coefficient matrix obtained from the low-dimension pseudo-measurement process and the corresponding solution is obtained analytically, respectively. The optimized sampling matrix is then dealt with non-negative constraint and binary quantization. Compared to the developed optimization schemes of speckle patterns, simulation results show that the proposed scheme can achieve better reconstruction quality with the low sampling rate in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity index (MSSIM). In particular, the lowest sampling rate we use to achieve a good performance is about 6.5%. At this sampling rate, the MSSIM and PSNR of the proposed scheme can reach 0.787 and 17.078 dB, respectively.
Highlights
High-quality reconstruction under a low sampling rate is very important for ghost imaging
Different from the dictionary learning scheme that optimizes the speckle patterns through the learned sparse basis or dictionary from a given training set[40], in this paper we propose a novel optimization scheme from the perspective of optimizing the sampling matrix and sparse basis, which is based on the measurement-driven framework (MDF)[43]
The sampling process in ghost imaging (GI) can be expressed a s45 y = x + ε where y is an M dimension column vector that denotes the signal measured by the bucket detector in the signal arm, is an M × N sampling matrix detected by the detector in the reference arm and preserves the light field intensity information, x is an N dimension column vector that stands for the object to be reconstructed, ε represents the additive noise
Summary
High-quality reconstruction under a low sampling rate is very important for ghost imaging. In this paper, inspired by matrix optimization in compressed sensing, an optimization scheme of speckle patterns via measurement-driven framework is introduced to improve the reconstruction quality of ghost imaging. According to this framework, the sampling matrix and sparse basis are optimized alternately using the sparse coefficient matrix obtained from the low-dimension pseudo-measurement process and the corresponding solution is obtained analytically, respectively. Compared to the developed optimization schemes of speckle patterns, simulation results show that the proposed scheme can achieve better reconstruction quality with the low sampling rate in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity index (MSSIM). Under the MDF, the sparse coefficient matrix is obtained by low-dimensional pseudo-measurement process and updated separately from the sparse basis
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