论文标题
通过复发性的结构约束，可靠的深层压缩感测
Robust Deep Compressive Sensing with Recurrent-Residual Structural Constraints
论文作者
论文摘要
现有的深层压缩传感（CS）方法可以忽略自适应在线优化或依赖重建过程中昂贵的迭代优化器。这项工作探索了一个新颖的图像CS框架，具有反复的残基结构约束，称为r $^2 $ cs-net。 R $^2 $ CS-NET首先通过新颖的复发性神经网络逐步优化了获得的采样。然后，级联的残留卷积网络从优化的潜在表示中充分重建图像。作为第一个Deep CS框架有效地桥接自适应在线优化，R $^2 $ CS-NET将在线优化的鲁棒性与深度学习方法的效率和非线性容量相结合。信号相关已通过网络体系结构解决。自适应感应性质进一步使其成为通过利用通道相关性的彩色图像CS的理想候选者。数值实验验证了所提出的复发潜在优化设计不仅可以实现适应动机，而且在同一情况下胜过经典的长期记忆（LSTM）体系结构。整个框架表明了硬件实施可行性，并具有现有深层CS基准的领先鲁棒性和概括能力。
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.
