  Deep CNNs have achieved significant successes in image processing and its
applications, including single image super-resolution (SR). However,
conventional methods still resort to some predetermined integer scaling
factors, e.g., x2 or x4. Thus, they are difficult to be applied when arbitrary
target resolutions are required. Recent approaches extend the scope to
real-valued upsampling factors, even with varying aspect ratios to handle the
limitation. In this paper, we propose the SRWarp framework to further
generalize the SR tasks toward an arbitrary image transformation. We interpret
the traditional image warping task, specifically when the input is enlarged, as
a spatially-varying SR problem. We also propose several novel formulations,
including the adaptive warping layer and multiscale blending, to reconstruct
visually favorable results in the transformation process. Compared with
previous methods, we do not constrain the SR model on a regular grid but allow
numerous possible deformations for flexible and diverse image editing.
Extensive experiments and ablation studies justify the necessity and
demonstrate the advantage of the proposed SRWarp method under various
transformations.
