论文标题
AMPA-NET：以深度压缩感应的优化启发的注意神经网络
AMPA-Net: Optimization-Inspired Attention Neural Network for Deep Compressed Sensing
论文作者
论文摘要
压缩传感（CS）是图像处理中的一个具有挑战性的问题，因为从有限的测量中重建了几乎完整的图像。为了实现快速准确的CS重建，我们合成了两种知名方法（神经网络和优化算法）的优势，以提出一种新型优化启发的神经网络，该神经网络称为AMP-NET。 AMP-NET意识到了近似消息传递（AMP）算法和神经网络的融合。其所有参数都是自动学习的。此外，我们提出了一个AMPA-NET，该AMPA网络使用三个注意网络来提高AMP-NET的表示能力。最后，我们证明了AMP-NET和AMPA-NET对四个标准CS重建基准数据集的有效性。我们的代码可在https://github.com/puallee/ampa-net上找到。
Compressed sensing (CS) is a challenging problem in image processing due to reconstructing an almost complete image from a limited measurement. To achieve fast and accurate CS reconstruction, we synthesize the advantages of two well-known methods (neural network and optimization algorithm) to propose a novel optimization inspired neural network which dubbed AMP-Net. AMP-Net realizes the fusion of the Approximate Message Passing (AMP) algorithm and neural network. All of its parameters are learned automatically. Furthermore, we propose an AMPA-Net which uses three attention networks to improve the representation ability of AMP-Net. Finally, We demonstrate the effectiveness of AMP-Net and AMPA-Net on four standard CS reconstruction benchmark data sets. Our code is available on https://github.com/puallee/AMPA-Net.
