  Single-pixel imaging (SPI) is an emerging technique which has attracts wide
attention in various research fields. However, restricted by the low
reconstruction quality and large amount of measurements, the practical
application is still in its infancy. Inspired by the fact that natural scenes
exhibit unique degenerate structures in the low dimensional subspace, we
propose to take advantage of the local prior in convolutional sparse coding to
implement high fidelity single-pixel imaging. Specifically, by statistically
learning strategy, the target scene can be sparse represented on an
overcomplete dictionary. The dictionary is composed of various basis learned
from a natural image database. We introduce the above local prior into
conventional SPI framework to promote the final reconstruction quality.
Experiments both on synthetic data and real captured data demonstrate that our
method can achieve better reconstruction from the same measurements, and thus
consequently reduce the number of required measurements for same reconstruction
quality.
