  In this paper, we present a dual-mode adaptive singular value decomposition
ghost imaging (A-SVD GI), which can be easily switched between the modes of
imaging and edge detection. It can adaptively localize the foreground pixels
via a threshold selection method. Then only the foreground region is
illuminated by the singular value decomposition (SVD) - based patterns,
consequently retrieving high-quality images with fewer sampling ratios. By
changing the selecting range of foreground pixels, the A-SVD GI can be switched
to the mode of edge detection to directly reveal the edge of objects, without
needing the original image. We investigate the performance of these two modes
through both numerical simulations and experiments. We also develop a
single-round scheme to halve measurement numbers in experiments, instead of
separately illuminating positive and negative patterns in traditional methods.
The binarized SVD patterns, generated by the spatial dithering method, are
modulated by a digital micromirror device (DMD) to speed up the data
acquisition. This dual-mode A-SVD GI can be applied in various applications,
such as remote sensing or target recognition, and could be further extended for
multi-modality functional imaging/detection.
