The Compression Sensing Reconstruction for the 1-d Signal Based on Non-local Full Connection Layer

The compression sensing reconstruction for the 1-d signal can contribute to the communication of autonomous driving, intelligent robots, and fire exploration robots. To address the issue that fully connected layers in the LISTA method lack the ability to extract non-local features, this paper primarily designs a non-local fully connection layer and proposes a novel compressed sensing reconstruction method for audio signals. This paper designs a compression sensing reconstruction method for the 1-d signal. To reconstruct 1-d signal, the deep learning method LISTA is used. Then, the linear full connection layer in LISTA is improved by combining the output of three full connection layer to capture the non-local information. The computing regions of improved non-local full connection layer contain: 1) the full connection before; 2) the current full connection; and 3) the full connection after. Experimental results show the reconstruction results of LISTA and LISTAₙf are both close to the real signal. The MSE of LISTAₙf is reduced by 0. 1 than the MSE of ISTA under the same experimental settings. The non-local full connection layer in the LISTAₙf consumes longer computing time. The LISTAₙf increase the computing time by 0. 07s than the computing time of the ISTA. Experimental results show the effectiveness of the proposed method.