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.

本文研究高清晰度重建中的量化质量和感知质量之间的平衡关系，并提出了一种新的超分辨率方法，该方法结合深度网络、鉴别器网络和两个定量评分预测器网络，可以在保持传统定量性能的同时提高上采样图像的感知质量。实验结果表明，所提出的方法在量化和感知质量之间取得了良好的平衡，比现有方法更令人满意。