  Recently, deep network-based image compressed sensing methods achieved high
reconstruction quality and reduced computational overhead compared with
traditional methods. However, existing methods obtain measurements only from
partial features in the network and use them only once for image
reconstruction. They ignore there are low, mid, and high-level features in the
network\cite{zeiler2014visualizing} and all of them are essential for
high-quality reconstruction. Moreover, using measurements only once may not be
enough for extracting richer information from measurements. To address these
issues, we propose a novel Measurements Reuse Convolutional Compressed Sensing
Network (MR-CCSNet) which employs Global Sensing Module (GSM) to collect all
level features for achieving an efficient sensing and Measurements Reuse Block
(MRB) to reuse measurements multiple times on multi-scale. Finally,
experimental results on three benchmark datasets show that our model can
significantly outperform state-of-the-art methods.
