Image compressed sensing reconstruction algorithm based on attention mechanism

Wenjie Yuan; Jinpeng Tian; Baojun Hou

doi:10.1117/12.2626665

20 December 2021 Image compressed sensing reconstruction algorithm based on attention mechanism

Wenjie Yuan, Jinpeng Tian, Baojun Hou

Proceedings Volume 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021); 1215507 (2021) https://doi.org/10.1117/12.2626665
Event: International Conference on Computer Vision, Application, and Design (CVAD 2021), 2021, Sanya, China

Abstract

Very deep convolutional neural networks (CNNs) have shown great power in image compressed sensing (CS) reconstruction and achieved significant improvements against traditional methods. Among these CNN-based methods, the number of convolutional feature maps is critical to the performance of the network. However, existing algorithms only perform average weighting processing on feature maps, and do not make full use of image feature differences to adaptively assign feature weights. To address this issue, we propose an attention mechanism network for image compression sensing reconstruction (AM-CSNet). AM-CSNet uses multiple attention modules (AM) to adaptively learn feature weights in the channel and spatial dimensions, which makes the model more lightweight and efficient. To maximize the performance of AM-CSNet, we use Residual Feature Aggregation Group (RFAG) to fully retain the features on different residual branches. Extensive CS experiments demonstrate that the proposed AM-CSNet is superior to many other state-of-the-art methods, such as TIP-CSNet and SCSNet.

Citation Download Citation

Wenjie Yuan, Jinpeng Tian, and Baojun Hou "Image compressed sensing reconstruction algorithm based on attention mechanism", Proc. SPIE 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021), 1215507 (20 December 2021); https://doi.org/10.1117/12.2626665

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KEYWORDS

Reconstruction algorithms

Compressed sensing

Image compression

Image processing

Image quality

Convolution

Image restoration

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