Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution. (arXiv:2108.02348v4 [eess.IV] UPDATED)

For deep learning methods of real-world image super-resolution, the most
critical issue is whether the paired low and high resolution images for
training accurately reflect the sampling process of real cameras. Low and high
resolution (LR$\sim$HR) image pairs synthesized by existing degradation models
(e.g., bicubic downsampling) deviate from those in reality; thus the
super-resolution CNN trained by these synthesized LR$\sim$HR image pairs does
not perform well when being applied to real images. To address the problem, we
propose a novel …

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