  Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a
single neural network for signal reconstruction, and fail to jointly consider
the influences of the sampling operation for reconstruction. In this paper, we
propose unified framework, which jointly considers the sampling and
reconstruction process for image compressive sensing based on well-designed
cascade neural networks. Two sub-networks, which are the sampling sub-network
and the reconstruction sub-network, are included in the proposed framework. In
the sampling sub-network, an adaptive full connected layer instead of the
traditional random matrix is used to mimic the sampling operator. In the
reconstruction sub-network, a cascade network combining stacked denoising
autoencoder (SDA) and convolutional neural network (CNN) is designed to
reconstruct signals. The SDA is used to solve the signal mapping problem and
the signals are initially reconstructed. Furthermore, CNN is used to fully
recover the structure and texture features of the image to obtain better
reconstruction performance. Extensive experiments show that this framework
outperforms many other state-of-the-art methods, especially at low sampling
rates.
