Existing deep compressive sensing (CS) methods either ignore adaptive online
optimization or depend on costly iterative optimizer during reconstruction.
This work explores a novel image CS framework with recurrent-residual
structural constraint, termed as R2CS-NET. The R2CS-NET first
progressively optimizes the acquired samplings through a novel recurrent neural
network. The cascaded residual convolutional network then fully reconstructs
the image from optimized latent representation. As the first deep CS framework
efficiently bridging adaptive online optimization, the R2CS-NET integrates
the robustness of online optimization with the efficiency and nonlinear
capacity of deep learning methods. Signal correlation has been addressed
through the network architecture. The adaptive sensing nature further makes it
an ideal candidate for color image CS via leveraging channel correlation.
Numerical experiments verify the proposed recurrent latent optimization design
not only fulfills the adaptation motivation, but also outperforms classic long
short-term memory (LSTM) architecture in the same scenario. The overall
framework demonstrates hardware implementation feasibility, with leading
robustness and generalization capability among existing deep CS benchmarks