  Recent studies have significantly enhanced the performance of single-image
super-resolution (SR) using convolutional neural networks (CNNs). While there
can be many high-resolution (HR) solutions for a given input, most existing
CNN-based methods do not explore alternative solutions during the inference. A
typical approach to obtaining alternative SR results is to train multiple SR
models with different loss weightings and exploit the combination of these
models. Instead of using multiple models, we present a more efficient method to
train a single adjustable SR model on various combinations of losses by taking
advantage of multi-task learning. Specifically, we optimize an SR model with a
conditional objective during training, where the objective is a weighted sum of
multiple perceptual losses at different feature levels. The weights vary
according to given conditions, and the set of weights is defined as a style
controller. Also, we present an architecture appropriate for this training
scheme, which is the Residual-in-Residual Dense Block equipped with spatial
feature transformation layers. At the inference phase, our trained model can
generate locally different outputs conditioned on the style control map.
Extensive experiments show that the proposed SR model produces various
desirable reconstructions without artifacts and yields comparable quantitative
performance to state-of-the-art SR methods.
