  Existing reference (RF)-based super-resolution (SR) models try to improve
perceptual quality in SR under the assumption of the availability of
high-resolution RF images paired with low-resolution (LR) inputs at testing. As
the RF images should be similar in terms of content, colors, contrast, etc. to
the test image, this hinders the applicability in a real scenario. Other
approaches to increase the perceptual quality of images, including perceptual
loss and adversarial losses, tend to dramatically decrease fidelity to the
ground-truth through significant decreases in PSNR/SSIM. Addressing both
issues, we propose a simple yet universal approach to improve the perceptual
quality of the HR prediction from a pre-trained SR network on a given LR input
by further fine-tuning the SR network on a subset of images from the training
dataset with similar patterns of activation as the initial HR prediction, with
respect to the filters of a feature extractor. In particular, we show the
effects of fine-tuning on these images in terms of the perceptual quality and
PSNR/SSIM values. Contrary to perceptually driven approaches, we demonstrate
that the fine-tuned network produces a HR prediction with both greater
perceptual quality and minimal changes to the PSNR/SSIM with respect to the
initial HR prediction. Further, we present novel numerical experiments
concerning the filters of SR networks, where we show through filter
correlation, that the filters of the fine-tuned network from our method are
closer to "ideal" filters, than those of the baseline network or a network
fine-tuned on random images.
