论文标题
超级分辨率的基于频域的感知损失
Frequency Domain-based Perceptual Loss for Super Resolution
论文作者
论文摘要
我们引入了频域知觉损失（FDPL），这是单个图像超级分辨率（SR）的损耗函数。与以前用于训练SR模型的损耗功能不同，这些功能都是在像素（空间）域中计算的，因此在频域中计算FDPL。通过在频域中工作，我们可以鼓励给定模型学习与人类感知最相关的频率优先级的映射。虽然FDPL的目标不是最大化峰信号与噪声比（PSNR），但我们发现降低FDPL与PSNR增加之间存在相关性。与在SET5 Image DataSet上测得的相同的模型相比，使用FDPL训练A模型会导致较高的平均PSRN（30.94）（30.94）（30.94）（30.59）。我们还表明，我们的方法可实现更高的定性结果，这是感知损失函数的目标。但是，尚不清楚提高的感知质量是由于PSNR稍高或FDPL的感知性质所致。
We introduce Frequency Domain Perceptual Loss (FDPL), a loss function for single image super resolution (SR). Unlike previous loss functions used to train SR models, which are all calculated in the pixel (spatial) domain, FDPL is computed in the frequency domain. By working in the frequency domain we can encourage a given model to learn a mapping that prioritizes those frequencies most related to human perception. While the goal of FDPL is not to maximize the Peak Signal to Noise Ratio (PSNR), we found that there is a correlation between decreasing FDPL and increasing PSNR. Training a model with FDPL results in a higher average PSRN (30.94), compared to the same model trained with pixel loss (30.59), as measured on the Set5 image dataset. We also show that our method achieves higher qualitative results, which is the goal of a perceptual loss function. However, it is not clear that the improved perceptual quality is due to the slightly higher PSNR or the perceptual nature of FDPL.
