  Recent years, compressive sensing (CS) has improved greatly for the
application of deep learning technology. For convenience, the input image is
usually measured and reconstructed block by block. This usually causes block
effect in reconstructed images. In this paper, we present a novel CNN-based
network to solve this problem. In measurement part, the input image is
adaptively measured block by block to acquire a group of measurements. While in
reconstruction part, all the measurements from one image are used to
reconstruct the full image at the same time. Different from previous method
recovering block by block, the structure information destroyed in measurement
part is recovered in our framework. Block effect is removed accordingly. We
train the proposed framework by mean square error (MSE) loss function.
Experiments show that there is no block effect at all in the proposed method.
And our results outperform 1.8 dB compared with existing methods.
