  Deep convolutional neural networks have significantly improved the peak
signal-to-noise ratio of SuperResolution (SR). However, image viewer
applications commonly allow users to zoom the images to arbitrary magnification
scales, thus far imposing a large number of required training scales at a
tremendous computational cost. To obtain a more computationally efficient model
for arbitrary scale SR, this paper employs a Laplacian pyramid method to
reconstruct any-scale high-resolution (HR) images using the high-frequency
image details in a Laplacian Frequency Representation. For SR of small-scales
(between 1 and 2), images are constructed by interpolation from a sparse set of
precalculated Laplacian pyramid levels. SR of larger scales is computed by
recursion from small scales, which significantly reduces the computational
cost. For a full comparison, fixed- and any-scale experiments are conducted
using various benchmarks. At fixed scales, ASDN outperforms predefined
upsampling methods (e.g., SRCNN, VDSR, DRRN) by about 1 dB in PSNR. At
any-scale, ASDN generally exceeds Meta-SR on many scales.
