  Recently, deep learning-based compressed sensing (CS) has achieved great
success in reducing the sampling and computational cost of sensing systems and
improving the reconstruction quality. These approaches, however, largely
overlook the issue of the computational cost; they rely on complex structures
and task-specific operator designs, resulting in extensive storage and high
energy consumption in CS imaging systems. In this paper, we propose a
lightweight but effective deep neural network based on recurrent learning to
achieve a sustainable CS system; it requires a smaller number of parameters but
obtains high-quality reconstructions. Specifically, our proposed network
consists of an initial reconstruction sub-network and a residual reconstruction
sub-network. While the initial reconstruction sub-network has a hierarchical
structure to progressively recover the image, reducing the number of
parameters, the residual reconstruction sub-network facilitates recurrent
residual feature extraction via recurrent learning to perform both feature
fusion and deep reconstructions across different scales. In addition, we also
demonstrate that, after the initial reconstruction, feature maps with reduced
sizes are sufficient to recover the residual information, and thus we achieved
a significant reduction in the amount of memory required. Extensive experiments
illustrate that our proposed model can achieve a better reconstruction quality
than existing state-of-the-art CS algorithms, and it also has a smaller number
of network parameters than these algorithms. Our source codes are available at:
https://github.com/C66YU/CSRN.
