  In recent years, deep learning-based image compressive sensing (ICS) methods
have achieved brilliant success. Many optimization-inspired networks have been
proposed to bring the insights of optimization algorithms into the network
structure design and have achieved excellent reconstruction quality with low
computational complexity. But they keep the information flow in pixel space as
traditional algorithms by updating and transferring the image in pixel space,
which does not fully use the information in the image features. In this paper,
we propose the idea of achieving information flow phase by phase in feature
space and design a Feature-Space Optimization-Inspired Network (dubbed FSOINet)
to implement it by mapping both steps of proximal gradient descent algorithm
from pixel space to feature space. Moreover, the sampling matrix is learned
end-to-end with other network parameters. Experiments show that the proposed
FSOINet outperforms the existing state-of-the-art methods by a large margin
both quantitatively and qualitatively. The source code is available on
https://github.com/cwjjun/FSOINet.
