Many service computing applications require real-time dataset collection from multiple devices, necessitating efficient sampling techniques to reduce bandwidth and storage pressure. Compressive sensing (CS) has found wide-ranging applications in image acquisition and reconstruction. Recently, numerous deep-learning methods have been introduced for CS tasks. However, the accurate reconstruction of images from measurements remains a significant challenge, especially at low sampling rates. In this paper, we propose Uformer-ICS as a novel U-shaped transformer for image CS tasks by introducing inner characteristics of CS into transformer architecture. To utilize the uneven sparsity distribution of image blocks, we design an adaptive sampling architecture that allocates measurement resources based on the estimated block sparsity, allowing the compressed results to retain maximum information from the original image. Additionally, we introduce a multi-channel projection (MCP) module inspired by traditional CS optimization methods. By integrating the MCP module into the transformer blocks, we construct projection-based transformer blocks, and then form a symmetrical reconstruction model using these blocks and residual convolutional blocks. Therefore, our reconstruction model can simultaneously utilize the local features and long-range dependencies of image, and the prior projection knowledge of CS theory. Experimental results demonstrate its significantly better reconstruction performance than state-of-the-art deep learning-based CS methods.

翻译：许多服务计算应用需要从多个设备实时采集数据集，这要求高效的采样技术以减轻带宽和存储压力。压缩感知（CS）在图像采集与重建中已得到广泛应用。近年来，众多深度学习方法被引入CS任务。然而，从测量值中精确重建图像仍然是一个重大挑战，尤其是在低采样率下。本文提出Uformer-ICS，一种用于图像CS任务的新型U型Transformer，其将CS的内在特性引入Transformer架构。为利用图像块的非均匀稀疏分布，我们设计了一种自适应采样架构，该架构根据估计的块稀疏度分配测量资源，使压缩结果能保留原始图像的最大信息量。此外，受传统CS优化方法启发，我们引入了多通道投影（MCP）模块。通过将MCP模块集成到Transformer块中，我们构建了基于投影的Transformer块，并利用这些块与残差卷积块共同构成对称的重建模型。因此，我们的重建模型能够同时利用图像的局部特征、长程依赖关系以及CS理论中的先验投影知识。实验结果表明，其重建性能显著优于当前最先进的基于深度学习的CS方法。