  Super-resolution (SR) is a technique that allows increasing the resolution of
a given image. Having applications in many areas, from medical imaging to
consumer electronics, several SR methods have been proposed. Currently, the
best performing methods are based on convolutional neural networks (CNNs) and
require extensive datasets for training. However, at test time, they fail to
impose consistency between the super-resolved image and the given
low-resolution image, a property that classic reconstruction-based algorithms
naturally enforce in spite of having poorer performance. Motivated by this
observation, we propose a new framework that joins both approaches and produces
images with superior quality than any of the prior methods. Although our
framework requires additional computation, our experiments on Set5, Set14, and
BSD100 show that it systematically produces images with better peak signal to
noise ratio (PSNR) and structural similarity (SSIM) than the current
state-of-the-art CNN architectures for SR.
