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 R$^2$CS-NET. The R$^2$CS-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 R$^2$CS-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.

本文提出了一种新的基于循环残差结构约束的图像压缩感知重构方法，称为 R^2CS-NET，该方法可以在适应性在线优化和图像重构中有效地整合在线优化的鲁棒性和深度学习方法的效率和非线性能力。该方法还可以通过利用通道相关性实现彩色图像压缩感知。经过实验验证，该方法具有较高的鲁棒性和泛化能力，可以在现有的深度压缩感知基准测试中表现出色。