  Assuming a known degradation model, the performance of a learned image
super-resolution (SR) model depends on how well the variety of image
characteristics within the training set matches those in the test set. As a
result, the performance of an SR model varies noticeably from image to image
over a test set depending on whether characteristics of specific images are
similar to those in the training set or not. Hence, in general, a single SR
model cannot generalize well enough for all types of image content. In this
work, we show that training multiple SR models for different classes of images
(e.g., for text, texture, etc.) to exploit class-specific image priors and
employing a post-processing network that learns how to best fuse the outputs
produced by these multiple SR models surpasses the performance of
state-of-the-art generic SR models. Experimental results clearly demonstrate
that the proposed multiple-model SR (MMSR) approach significantly outperforms a
single pre-trained state-of-the-art SR model both quantitatively and visually.
It even exceeds the performance of the best single class-specific SR model
trained on similar text or texture images.
