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arXiv · 1809.04789

Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality

Jun-Ho Choi·Jun-Hyuk Kim·Manri Cheon·Jong-Seok Lee
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Abstract

Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspond to the similarity to the ground-truth images and the naturalness, respectively. In this paper, we propose a novel super-resolution method that can improve the perceptual quality of the upscaled images while preserving the conventional quantitative performance. The proposed method employs a deep network for multi-pass upscaling in company with a discriminator network and two quantitative score predictor networks. Experimental results demonstrate that the proposed method achieves a good balance of the quantitative and perceptual quality, showing more satisfactory results than existing methods.

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Jun-Ho Choi, Jun-Hyuk Kim, Manri Cheon, Jong-Seok Lee. 2018-09-13. Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality. https://arxiv.org/abs/1809.04789

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