Image Reconstruction of IoT based on Parallel CNN

As an effective signal acquisition and reconstruction scheme, compressed sensing (CS) is widely used in measurement and reconstruction of Internet of things (IoT). CS can recover images from fewer measurements compared with traditional signal acquisition and reconstruction methods. Recently, many deep learning based CS methods are proposed for image reconstruction and achieve better performance compared with traditional CS reconstruction methods. However these methods usually divide the image into blocks and utilize random measurement matrix for block measurement, which ignores the correlation between blocks. Furthermore, some existing reconstruction methods based on deep learning only adopt simple channel convolutional neural networks (CNN) to complete image reconstruction, which does not make full use of CNN's presentation ability. In order to solve the above problems, we proposes a novel image measurement and reconstruction framework to achieve high quaulity reconstruction. In measurement part, we use a convolutional layer instead of random measurement matrix to directly acquire all measurements which provides more construction information for subsequent image reconstruction and removes block effect. In reconstruction part, this paper firstly uses a deconvolution layer to obtain an initial reconstructed image which has same dimension as the input image. Then we employ multiple parallel CNN to obtain multiple feature information. The multiple parallel CNN include dilated convolution kernels of different receptive field to increase the network's receptive field, which can obtain more image structural information for reconstruction. The results show that the performance of image reconstruction is greatly ameliorated compared with the existing the state-of-the-art methods.