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This work identifies and addresses two important technical challenges insingle-image super-resolution: (1) how to upsample an image without magnifyingnoise and (2) how to preserve large scale structure when upsampling. Wesummarize the techniques we developed for our second place entry in Track 1(Bicubic Downsampling), seventh place entry in Track 2 (Realistic AdverseConditions), and seventh place entry in Track 3 (Realistic difficult) in the2018 NTIRE Super-Resolution Challenge. Furthermore, we present new neuralnetwork architectures that specifically address the two challenges listedabove: denoising and preservation of large-scale structure.

New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution