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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.

We show, for the non-Markovian or time-dependent Markovian process, by breaking up a spatial quantum channel (SQC) into a series of periodically arranged sub-components, that the quality of information transmission described by the purity, fidelity and concurrence of the output states can be improved. The relation to the quantum zero effect and possible applications of the idea have been discussed., Comment: 6 pages, 4 figutes