  Blind super-resolution (SR) methods aim to generate a high quality high
resolution image from a low resolution image containing unknown degradations.
However, natural images contain various types and amounts of blur: some may be
due to the inherent degradation characteristics of the camera, but some may
even be intentional, for aesthetic purposes (e.g. Bokeh effect). In the case of
the latter, it becomes highly difficult for SR methods to disentangle the blur
to remove, and that to leave as is. In this paper, we propose a novel blind SR
framework based on kernel-oriented adaptive local adjustment (KOALA) of SR
features, called KOALAnet, which jointly learns spatially-variant degradation
and restoration kernels in order to adapt to the spatially-variant blur
characteristics in real images. Our KOALAnet outperforms recent blind SR
methods for synthesized LR images obtained with randomized degradations, and we
further show that the proposed KOALAnet produces the most natural results for
artistic photographs with intentional blur, which are not over-sharpened, by
effectively handling images mixed with in-focus and out-of-focus areas.
