  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.
