  The performance of deep learning based image super-resolution (SR) methods
depend on how accurately the paired low and high resolution images for training
characterize the sampling process of real cameras. Low and high resolution
(LR$\sim$HR) image pairs synthesized by degradation models (e.g., bicubic
downsampling) deviate from those in reality; thus the synthetically-trained
DCNN SR models work disappointingly when being applied to real-world images. To
address this issue, we propose a novel data acquisition process to shoot a
large set of LR$\sim$HR image pairs using real cameras. The images are
displayed on an ultra-high quality screen and captured at different
resolutions. The resulting LR$\sim$HR image pairs can be aligned at very high
sub-pixel precision by a novel spatial-frequency dual-domain registration
method, and hence they provide more appropriate training data for the learning
task of super-resolution. Moreover, the captured HR image and the original
digital image offer dual references to strengthen supervised learning.
Experimental results show that training a super-resolution DCNN by our
LR$\sim$HR dataset achieves higher image quality than training it by other
datasets in the literature. Moreover, the proposed screen-capturing data
collection process can be automated; it can be carried out for any target
camera with ease and low cost, offering a practical way of tailoring the
training of a DCNN SR model separately to each of the given cameras.
