ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing
ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing
复制标题
ADMM-CSNet：图像压缩感知的深度学习方法
DOI：
10.1109/tpami.2018.2883941
复制
发表时间：
2020-03-01
影响因子：
23.6
通讯作者：
Xu, Zongben
中科院分区：
文献类型：
--
作者：
Yang, Yan;Sun, Jian;Xu, Zongben
压缩感知（CS）是一种从少量采样数据中重建图像的有效技术。它已广泛应用于医学成像、遥感、图像压缩等领域。在本文中，我们通过将传统的基于模型的CS方法和数据驱动的深度学习方法相结合，提出了一种新颖的深度学习架构的两个版本，称为ADMM - CSNet，用于从稀疏采样测量中重建图像。我们首先考虑一个在不确定变换域中具有不确定正则化的广义CS图像重建模型，然后提出了两个使用交替方向乘子法（ADMM）算法优化该模型的有效求解器。我们进一步将ADMM算法展开并推广为两种深度架构，其中CS模型和ADMM算法的所有参数都通过端到端训练进行判别式学习。对于快速CS复值磁共振成像和实值自然图像的CS成像这两种应用，与传统方法和其他深度学习方法相比，所提出的ADMM - CSNet在快速计算速度下实现了良好的重建精度。
Compressive sensing (CS) is an effective technique for reconstructing image from a small amount of sampled data. It has been widely applied in medical imaging, remote sensing, image compression, etc. In this paper, we propose two versions of a novel deep learning architecture, dubbed as ADMM-CSNet, by combining the traditional model-based CS method and data-driven deep learning method for image reconstruction from sparsely sampled measurements. We first consider a generalized CS model for image reconstruction with undetermined regularizations in undetermined transform domains, and then two efficient solvers using Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing the model are proposed. We further unroll and generalize the ADMM algorithm to be two deep architectures, in which all parameters of the CS model and the ADMM algorithm are discriminatively learned by end-to-end training. For both applications of fast CS complex-valued MR imaging and CS imaging of real-valued natural images, the proposed ADMM-CSNet achieved favorable reconstruction accuracy in fast computational speed compared with the traditional and the other deep learning methods.