  Conventional computational ghost imaging (CGI) uses light carrying a sequence
of patterns with uniform-resolution to illuminate the object, then performs
correlation calculation based on the light intensity value reflected by the
target and the preset patterns to obtain object image. It requires a large
number of measurements to obtain high-quality images, especially if
high-resolution images are to be obtained. To solve this problem, we developed
temporally variable-resolution illumination patterns, replacing the
conventional uniform-resolution illumination patterns with a sequence of
patterns of different imaging resolutions. In addition, we propose to combine
temporally variable-resolution illumination patterns and spatially
variable-resolution structure to develop temporally and spatially
variable-resolution (TSV) illumination patterns, which not only improve the
imaging quality of the region of interest (ROI) but also improve the robustness
to noise. The methods using proposed illumination patterns are verified by
simulations and experiments compared with CGI. For the same number of
measurements, the method using temporally variable-resolution illumination
patterns has better imaging quality than CGI, but it is less robust to noise.
The method using TSV illumination patterns has better imaging quality in ROI
than the method using temporally variable-resolution illumination patterns and
CGI under the same number of measurements. We also experimentally verify that
the method using TSV patterns have better imaging performance when applied to
higher resolution imaging. The proposed methods are expected to solve the
current computational ghost imaging that is difficult to achieve
high-resolution and high-quality imaging.
