  Deep learning based single image super-resolution (SR) methods have been
rapidly evolved over the past few years and have yielded state-of-the-art
performances over conventional methods. Since these methods usually minimized
l1 loss between the output SR image and the ground truth image, they yielded
very high peak signal-to-noise ratio (PSNR) that is inversely proportional to
these losses. Unfortunately, minimizing these losses inevitably lead to blurred
edges due to averaging of plausible solutions. Recently, SRGAN was proposed to
avoid this average effect by minimizing perceptual losses instead of l1 loss
and it yielded perceptually better SR images (or images with sharp edges) at
the price of lowering PSNR. In this paper, we propose SREdgeNet, edge enhanced
single image SR network, that was inspired by conventional SR theories so that
average effect could be avoided not by changing the loss, but by changing the
SR network property with the same l1 loss. Our SREdgeNet consists of 3
sequential deep neural network modules: the first module is any
state-of-the-art SR network and we selected a variant of EDSR. The second
module is any edge detection network taking the output of the first SR module
as an input and we propose DenseEdgeNet for this module. Lastly, the third
module is merging the outputs of the first and second modules to yield edge
enhanced SR image and we propose MergeNet for this module. Qualitatively, our
proposed method yielded images with sharp edges compared to other
state-of-the-art SR methods. Quantitatively, our SREdgeNet yielded
state-of-the-art performance in terms of structural similarity (SSIM) while
maintained comparable PSNR for x8 enlargement.
