基于超深残差通道注意力网络的图像压缩感知重构

doi:10.3969/j.issn.0255-8297.2022.06.001

Abstract

Abstract: An image compressive sensing reconstruction method based on ultra-deep residual channel attention networks is proposed. The reconstruction part of the ultra-deep residual channel attention network consists of multiple residual groups, each of which contains a long connection and a set of residual blocks with short connections. The long-connected structure can effectively deliver rich low-frequency information, allowing main network to focus on learning high-frequency information. The channel attention mechanism is introduced into residual blocks. By considering the interdependence between channels, the channel features keep changing adaptively so as to strengthen the important features. Experiments show that this method can effectively improve the reconstruction accuracy of image compressed sensing.

Key words: compressive sensing, image reconstruction, residual group, channel attention (CA)

CLC Number:

TN911.73

YUAN Wenjie, TIAN Jinpeng, YANG Jie. Image Compressive Sensing Reconstruction Using Ultra-Deep Residual Channel Attention Network[J]. Journal of Applied Sciences, 2022, 40(6): 887-895.

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Image Compressive Sensing Reconstruction Using Ultra-Deep Residual Channel Attention Network

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