  Deep Convolutional Neural Networks (CNNs) have achieved remarkable results on
Single Image Super-Resolution (SISR). Despite considering only a single
degradation, recent studies also include multiple degrading effects to better
reflect real-world cases. However, most of the works assume a fixed combination
of degrading effects, or even train an individual network for different
combinations. Instead, a more practical approach is to train a single network
for wide-ranging and variational degradations. To fulfill this requirement,
this paper proposes a unified network to accommodate the variations from
inter-image (cross-image variations) and intra-image (spatial variations).
Different from the existing works, we incorporate dynamic convolution which is
a far more flexible alternative to handle different variations. In SISR with
non-blind setting, our Unified Dynamic Convolutional Network for Variational
Degradations (UDVD) is evaluated on both synthetic and real images with an
extensive set of variations. The qualitative results demonstrate the
effectiveness of UDVD over various existing works. Extensive experiments show
that our UDVD achieves favorable or comparable performance on both synthetic
and real images.
