  Convolutional neural network (CNN) based methods have recently achieved great
success for image super-resolution (SR). However, most deep CNN based SR models
attempt to improve distortion measures (e.g. PSNR, SSIM, IFC, VIF) while
resulting in poor quantified perceptual quality (e.g. human opinion score,
no-reference quality measures such as NIQE). Few works have attempted to
improve the perceptual quality at the cost of performance reduction in
distortion measures. A very recent study has revealed that distortion and
perceptual quality are at odds with each other and there is always a trade-off
between the two. Often the restoration algorithms that are superior in terms of
perceptual quality, are inferior in terms of distortion measures. Our work
attempts to analyze the trade-off between distortion and perceptual quality for
the problem of single image SR. To this end, we use the well-known SR
architecture-enhanced deep super-resolution (EDSR) network and show that it can
be adapted to achieve better perceptual quality for a specific range of the
distortion measure. While the original network of EDSR was trained to minimize
the error defined based on per-pixel accuracy alone, we train our network using
a generative adversarial network framework with EDSR as the generator module.
Our proposed network, called enhanced perceptual super-resolution network
(EPSR), is trained with a combination of mean squared error loss, perceptual
loss, and adversarial loss. Our experiments reveal that EPSR achieves the
state-of-the-art trade-off between distortion and perceptual quality while the
existing methods perform well in either of these measures alone.
