Abstract: Aiming at the problems of low refinement of image reconstruction and weak network generalization ability in compressive sensing reconstruction algorithms based on deep learning, a compressive sensing  image reconstruction network (EH-ISTANet) based on iterative shrinkage thresholding and deep learning  is proposed. The model consists of three parts: extraction subnetwork, initialization subnetwork and enhancement reconstruction subnetwork. It adds the attention mechanism and cooperates with the neighborhood mapping module to send the obtained features to the enhancement module, so as to enhance the edge and texture of the reconstructed image. The reconstruction stage mimics the unfolding process of the traditional iterative shrinkage thresholding algorithm, and each stage can flexibly model the measurement matrix and dynamically adjust the step size in the gradient descent step. It is verified that the peak signal-to-noise ratio of the model is improved in different datasets with different sampling rates. It is demonstrated that the model outperforms other models in improving generalization ability and reconstruction accuracy. When the compressive sensing rate is 10%, the average signal-to-noise ratio of this model on five testsets is improved by 1.69 dB, 4.36 dB and 1.93 dB compared with CSNet, AMP-Net, and AMP-Net-BM models.

Key words: compressive sensing, deep learning, attention mechanism, feature enhancement