You Di, Zhang Jian, Xie Jingfen, Chen Bin, Ma Siwei
IEEE Trans Image Process. 2021;30:6066-6080. doi: 10.1109/TIP.2021.3091834. Epub 2021 Jul 5.
Recent deep network-based compressive sensing (CS) methods have achieved great success. However, most of them regard different sampling matrices as different independent tasks and need to train a specific model for each target sampling matrix. Such practices give rise to inefficiency in computing and suffer from poor generalization ability. In this paper, we propose a novel COntrollable Arbitrary-Sampling neTwork, dubbed COAST, to solve CS problems of arbitrary-sampling matrices (including unseen sampling matrices) with one single model. Under the optimization-inspired deep unfolding framework, our COAST exhibits good interpretability. In COAST, a random projection augmentation (RPA) strategy is proposed to promote the training diversity in the sampling space to enable arbitrary sampling, and a controllable proximal mapping module (CPMM) and a plug-and-play deblocking (PnP-D) strategy are further developed to dynamically modulate the network features and effectively eliminate the blocking artifacts, respectively. Extensive experiments on widely used benchmark datasets demonstrate that our proposed COAST is not only able to handle arbitrary sampling matrices with one single model but also to achieve state-of-the-art performance with fast speed.
最近基于深度网络的压缩感知（CS）方法取得了巨大成功。然而，其中大多数方法将不同的采样矩阵视为不同的独立任务，并且需要为每个目标采样矩阵训练一个特定的模型。这种做法导致计算效率低下，并且泛化能力较差。在本文中，我们提出了一种新颖的可控任意采样网络，称为COAST，以用单个模型解决任意采样矩阵（包括未见采样矩阵）的CS问题。在受优化启发的深度展开框架下，我们的COAST具有良好的可解释性。在COAST中，提出了一种随机投影增强（RPA）策略，以促进采样空间中的训练多样性，从而实现任意采样，并且进一步开发了可控近端映射模块（CPMM）和即插即用去块（PnP-D）策略，分别动态调制网络特征并有效消除块状伪影。在广泛使用的基准数据集上进行的大量实验表明，我们提出的COAST不仅能够用单个模型处理任意采样矩阵，而且能够以快速的速度实现领先的性能。
