  Recently, deep learning methods have made a significant improvement in
compressive sensing image reconstruction task. In the existing methods, the
scene is measured block by block due to the high computational complexity. This
results in block-effect of the recovered images. In this paper, we propose a
fully convolutional measurement network, where the scene is measured as a
whole. The proposed method powerfully removes the block-effect since the
structure information of scene images is preserved. To make the measure more
flexible, the measurement and the recovery parts are jointly trained. From the
experiments, it is shown that the results by the proposed method outperforms
those by the existing methods in PSNR, SSIM, and visual effect.
