计算机科学
重新使用
水准点(测量)
压缩传感
块(置换群论)
架空(工程)
编码(集合论)
GSM网络
数据挖掘
迭代重建
图像(数学)
计算机工程
人工智能
实时计算
计算机网络
工程类
程序设计语言
几何学
数学
大地测量学
集合(抽象数据类型)
操作系统
地理
废物管理
作者
Zi-En Fan,Feng Lian,Jia-Ni Quan
标识
DOI:10.1109/cvpr52688.2022.00875
摘要
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 it only once for image reconstruction. They ignore there are low, mid, and high-level features in the network [38] 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, we conduct a series of experiments on three benchmark datasets to show that our model can significantly outperform state-of-the-art methods. Code is available at: https://github.com/fze0012/MR-CCSNet.
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