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.
翻译:最近,基于网络的深层图像压缩遥感方法实现了高重建质量并减少了与传统方法相比的计算间接费用;然而,现有方法仅从网络的部分特征中获取测量数据,仅用于一次图像重建;它们忽视网络中低、中、高层次的特征,所有这些特征都对高质量的重建至关重要;此外,仅使用一次测量数据可能不足以从测量中提取更丰富的信息;为解决这些问题,我们提议建立一个新型的“计量再利用革命压缩遥感网络”,利用全球遥感模块收集所有层面的特征,以实现高效的遥感和计量再利用区块(MRB),以在多尺度上重复进行多次测量;最后,三个基准数据集的实验结果显示,我们的模型可以大大超过最先进的方法。