  In this paper, we present a novel deep learning-based approach for still
image super-resolution, that unlike the mainstream models does not rely solely
on the input low resolution image for high quality upsampling, and takes
advantage of a set of artificially created auxiliary self-replicas of the input
image that are incorporated in the neural network to create an enhanced and
accurate upscaling scheme. Inclusion of the proposed lossless pooling layers,
and the fusion of the input self-replicas enable the model to exploit the high
correlation between multiple instances of the same content, and eventually
result in significant improvements in the quality of the super-resolution,
which is confirmed by extensive evaluations.
