  We interpret convolutional networks as adaptive filters and combine them with
so-called MuxOut layers to efficiently upscale low resolution images. We
formalize this interpretation by deriving a linear and space-variant structure
of a convolutional network when its activations are fixed. We introduce general
purpose algorithms to analyze a network and show its overall filter effect for
each given location. We use this analysis to evaluate two types of image
upscalers: deterministic upscalers that target the recovery of details from
original content; and second, a new generation of upscalers that can sample the
distribution of upscale aliases (images that share the same downscale version)
that look like real content.
