  Ghost imaging (GI) is a novel imaging technique based on the second-order
correlation of light fields. Due to limited number of samplings in practice,
traditional GI methods often reconstruct objects with unsatisfactory quality.
To improve the imaging results, many reconstruction methods have been
developed, yet the reconstruction quality is still fundamentally restricted by
the modulated light fields. In this paper, we propose to improve the imaging
quality of GI by optimizing the light fields, which is realized via matrix
optimization for a learned dictionary incorporating the sparsity prior of
objects. A closed-form solution of the sampling matrix, which enables
successive sampling, is derived. Through simulation and experimental results,
it is shown that the proposed scheme leads to better imaging quality compared
to the state-of-the-art optimization methods for light fields, especially at a
low sampling rate.
