  Incorporating deep neural networks in image compressive sensing (CS) receives
intensive attentions in multimedia technology and applications recently. As
deep network approaches learn the inverse mapping directly from the CS
measurements, the reconstruction speed is significantly faster than the
conventional CS algorithms. However, for existing network based approaches, a
CS sampling procedure has to map a separate network model. This may potentially
degrade the performance of image CS with block-wise sampling because of
blocking artifacts, especially when multiple sampling rates are assigned to
different blocks within an image. In this paper, we develop a multichannel deep
network for block-based image CS by exploiting inter-block correlation with
performance significantly exceeding the current state-of-the-art methods. The
significant performance improvement is attributed to block-wise approximation
but full image removal of blocking artifacts. Specifically, with our
multichannel structure, the image blocks with a variety of sampling rates can
be reconstructed in a single model. The initially reconstructed blocks are then
capable of being reassembled into a full image to improve the recovered images
by unrolling a hand-designed block based CS recovery algorithm. Experimental
results demonstrate that the proposed method outperforms the state-of-the-art
CS methods by a large margin in terms of objective metrics and subjective
visual image quality. Our source codes are available at
https://github.com/siwangzhou/DeepBCS.
