  Taking photos of optoelectronic displays is a direct and spontaneous way of
transferring data and keeping records, which is widely practiced. However, due
to the analog signal interference between the pixel grids of the display screen
and camera sensor array, objectionable moir\'e (alias) patterns appear in
captured screen images. As the moir\'e patterns are structured and highly
variant, they are difficult to be completely removed without affecting the
underneath latent image. In this paper, we propose an approach of deep
convolutional neural network for demoir\'eing screen photos. The proposed DCNN
consists of a coarse-scale network and a fine-scale network. In the
coarse-scale network, the input image is first downsampled and then processed
by stacked residual blocks to remove the moir\'e artifacts. After that, the
fine-scale network upsamples the demoir\'ed low-resolution image back to the
original resolution. Extensive experimental results have demonstrated that the
proposed technique can efficiently remove the moir\'e patterns for camera
acquired screen images; the new technique outperforms the existing ones.
