  Deep convolutional neural networks achieve excellent image up-sampling
performance. However, CNN-based methods tend to restore high-resolution results
highly depending on traditional interpolations (e.g. bicubic). In this paper,
we present a deep sampling network (DSN) for down-sampling and up-sampling
without any cheap interpolation. First, the down-sampling subnetwork is trained
without supervision, thereby preserving more information and producing better
visual effects in the low-resolution image. Second, the up-sampling subnetwork
learns a sub-pixel residual with dense connections to accelerate convergence
and improve performance. DSN's down-sampling subnetwork can be used to generate
photo-realistic low-resolution images and replace traditional down-sampling
method in image processing. With the powerful down-sampling process, the
co-training DSN set a new state-of-the-art performance for image
super-resolution. Moreover, DSN is compatible with existing image codecs to
improve image compression.
