论文标题
使用显式感知损失的图像超分辨率
Image Super-Resolution using Explicit Perceptual Loss
论文作者
论文摘要
本文提出了一种优化用于生成视觉令人愉悦图像的超分辨率网络的明确方法。先前的方法使用多种损失功能，这些损失功能很难解释，并且具有隐含的关系以提高感知得分。我们展示了如何利用基于机器学习的模型，该模型经过直接训练以在生成的图像上提供感知得分。人们认为，这些模型可用于优化更易于解释的超分辨率网络。我们进一步分析了现有损失的特征以及我们提出的明确感知损失，以更好地解释。实验结果表明，显式方法的感知得分高于其他方法。最后，我们使用主观评估证明了显式感知损失和视觉上令人愉悦的图像的关系。
This paper proposes an explicit way to optimize the super-resolution network for generating visually pleasing images. The previous approaches use several loss functions which is hard to interpret and has the implicit relationships to improve the perceptual score. We show how to exploit the machine learning based model which is directly trained to provide the perceptual score on generated images. It is believed that these models can be used to optimizes the super-resolution network which is easier to interpret. We further analyze the characteristic of the existing loss and our proposed explicit perceptual loss for better interpretation. The experimental results show the explicit approach has a higher perceptual score than other approaches. Finally, we demonstrate the relation of explicit perceptual loss and visually pleasing images using subjective evaluation.
