Compressive sensing image reconstruction based on deep unfolding self-attention network

Convolutional neural network applied to image compressive sensing reconstruction at low sampling rate，which contains less information，and it is difficult for the reconstruction network to pay attention to the context information of the image. To overcome this problem，a compressive sensing image reconstruction based on deep unfolded self-attention network is proposed. The network combines sampling matrix and self-attention mechanism for image depth reconstruction，and fully utilizes the information of measurement values through multi-stage reconstruction module to enhance the quality of image reconstruction. The experimental results show that the network proposed can make full use of the sampling information of the image，outperform the existing state-of-the-art methods on different datasets，and the visual effect of the reconstructed image is better. © 2024 Editorial Board of Jilin University. All rights reserved.