论文标题
全局感测和测量值重复使用图像压缩感应
Global Sensing and Measurements Reuse for Image Compressed Sensing
论文作者
论文摘要
最近，与传统方法相比，基于网络的图像压缩传感方法可实现高重建质量和降低的计算开销。但是，现有方法仅从网络中的部分特征中获得测量结果，并仅将它们用于图像重建。他们忽略了网络\ cite {zeiler2014Visalization}的低，中和高级特征，所有这些特征对于高质量重建至关重要。此外，仅使用一次测量可能不足以从测量中提取更丰富的信息。为了解决这些问题，我们提出了一种新颖的测量值重复使用卷积压缩传感网络（MR-CCSNET），该网络（MR-CCSNET）采用全球传感模块（GSM）收集所有级别特征，以实现有效的感应和测量重用块（MRB）以多次在多尺度上重复使用测量。最后，三个基准数据集的实验结果表明，我们的模型可以显着胜过最先进的方法。
Recently, deep network-based image compressed sensing methods achieved high reconstruction quality and reduced computational overhead compared with traditional methods. However, existing methods obtain measurements only from partial features in the network and use them only once for image reconstruction. They ignore there are low, mid, and high-level features in the network\cite{zeiler2014visualizing} and all of them are essential for high-quality reconstruction. Moreover, using measurements only once may not be enough for extracting richer information from measurements. To address these issues, we propose a novel Measurements Reuse Convolutional Compressed Sensing Network (MR-CCSNet) which employs Global Sensing Module (GSM) to collect all level features for achieving an efficient sensing and Measurements Reuse Block (MRB) to reuse measurements multiple times on multi-scale. Finally, experimental results on three benchmark datasets show that our model can significantly outperform state-of-the-art methods.
