  Ghost imaging (GI) reconstructs images using a single-pixel or bucket
detector, which has the advantages of scattering robustness, wide spectrum and
beyond-visual-field imaging. However, this technique needs large amount of
measurements to obtain a sharp image. There have been a lot of methods proposed
to overcome this disadvantage. Retina-like patterns, as one of the compressive
sensing approaches, enhance the imaging quality of region of interest (ROI)
while not increase measurements. The design of the retina-like patterns
determines the performance of the ROI in the reconstructed image. Unlike the
conventional method to fill in ROI with random patterns, we propose to optimize
retina-like patterns by filling in the ROI with the patterns containing the
sparsity prior of objects. This proposed method is verified by simulations and
experiments compared with conventional GI, retina-like GI and GI using patterns
optimized by principal component analysis. The method using optimized
retina-like patterns obtain the best imaging quality in ROI than other methods.
Meanwhile, the good generalization ability of the optimized retina-like pattern
is also verified. While designing the size and position of the ROI of
retina-like pattern, the feature information of the target can be obtained to
optimize the pattern of ROI. This proposed method paves the way for realizing
high-quality GI.
