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.
Choi et.al REF trains a multiscale super-resolution model with a discriminator network and two qualitative score predictors, which achieves high perceptual quality while preserving the objective quality.
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality
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