Uformer-ICS: A U-Shaped Transformer for Image Compressive Sensing Service

计算机科学 变压器 压缩传感 电气工程 人工智能 电压 工程类
作者
Kuiyuan Zhang,Zhongyun Hua,Yuanman Li,Yushu Zhang,Yicong Zhou
出处
期刊:IEEE Transactions on Services Computing [Institute of Electrical and Electronics Engineers]
卷期号:17 (5): 2974-2988 被引量:2
标识
DOI:10.1109/tsc.2023.3334446
摘要

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.

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