Block compressive sensing (CS) is a well-known signal acquisition and reconstruction paradigm with widespread application prospects in science, engineering, and cybernetic systems. However, state-of-the-art block-based image CS (BCS) methods generally suffer from two issues. The sparsifying domain and the sensing matrices widely used for image acquisition are not data driven and, thus, both the features of the image and the relationships among subblock images are ignored. Moreover, it requires to address a high-dimensional optimization problem with extensive computational complexity for image reconstruction. In this article, we provide a deep learning (DL) strategy for BCS, called AutoBCS, which automatically takes the prior knowledge of images into account in the acquisition step and establishes a reconstruction model for performing fast image reconstruction. More precisely, we present a learning-based sensing matrix to accomplish image acquisition, thereby capturing and preserving more image characteristics than those captured by the existing methods. In addition, we build a noniterative reconstruction network, which provides an end-to-end BCS reconstruction framework to maximize image reconstruction efficiency. Furthermore, we investigate comprehensive comparison studies with both traditional BCS approaches and newly developed DL methods. Compared with these approaches, our proposed AutoBCS can not only provide superior performance in terms of image quality metrics (SSIM and PSNR) and visual perception but also automatically benefit reconstruction speed.

中文翻译：

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AutoBCS：具有数据驱动采集和非迭代重建的基于块的图像压缩感知


块压缩感知（CS）是一种众所周知的信号采集和重建范例，在科学、工程和控制论系统中具有广泛的应用前景。然而，最先进的基于块的图像 CS (BCS) 方法通常存在两个问题。广泛用于图像采集的稀疏域和传感矩阵不是数据驱动的，因此，图像的特征和子块图像之间的关系都被忽略。此外，它需要解决具有大量计算复杂性的高维优化问题以进行图像重建。在本文中，我们为BCS提供了一种深度学习（DL）策略，称为AutoBCS，它在采集步骤中自动考虑图像的先验知识，并建立用于执行快速图像重建的重建模型。更准确地说，我们提出了一种基于学习的传感矩阵来完成图像采集，从而捕获并保留比现有方法捕获的更多图像特征。此外，我们构建了一个非迭代重建网络，它提供了端到端的BCS重建框架，以最大限度地提高图像重建效率。此外，我们还调查了传统 BCS 方法和新开发的 DL 方法的综合比较研究。与这些方法相比，我们提出的 AutoBCS 不仅可以在图像质量指标（SSIM 和 PSNR）和视觉感知方面提供卓越的性能，而且还可以自动提高重建速度。