  We introduce a new learning strategy for image enhancement by recurrently
training the same simple superresolution (SR) network multiple times. After
initially training an SR network by using pairs of a corrupted low resolution
(LR) image and an original image, the proposed method makes use of the trained
SR network to generate new high resolution (HR) images with a doubled
resolution from the original uncorrupted images. Then, the new HR images are
downscaled to the original resolution, which work as target images for the SR
network in the next stage. The newly generated HR images by the repeatedly
trained SR network show better image quality and this strategy of training LR
to mimic new HR can lead to a more efficient SR network. Up to a certain point,
by repeating this process multiple times, better and better images are
obtained. This recurrent leaning strategy for SR can be a good solution for
downsizing convolution networks and making a more efficient SR network. To
measure the enhanced image quality, for the first time in this area of
super-resolution and image enhancement, we use VIQET MOS score which reflects
human visual quality more accurately than the conventional MSE measure.
