  Compressed Sensing (CS) theory simultaneously realizes the signal sampling
and compression process, and can use fewer observations to achieve accurate
signal recovery, providing a solution for better and faster transmission of
massive data. In this paper, a ternary sampling matrix-based method with
attention mechanism is proposed with the purpose to solve the problem that the
CS sampling matrices in most cases are random matrices, which are irrelative to
the sampled signal and need a large storage space. The proposed method consists
of three components, i.e., ternary sampling, initial reconstruction and deep
reconstruction, with the emphasis on the ternary sampling. The main idea of the
ternary method (-1, 0, +1) is to introduce the attention mechanism to evaluate
the importance of parameters at the sampling layer after the sampling matrix is
binarized (-1, +1), followed by pruning weight of parameters, whose importance
is below a predefined threshold, to achieve ternarization. Furthermore, a
compressed sensing algorithm especially for image reconstruction is
implemented, on the basis of the ternary sampling matrix, which is called
ATP-Net, i.e., Attention-based ternary projection network. Experimental results
show that the quality of image reconstruction by means of ATP-Net maintains a
satisfactory level with the employment of the ternary sampling matrix, i.e.,
the average PSNR on Set11 is 30.4 when the sampling rate is 0.25, approximately
6% improvement compared with that of DR2-Net.
