论文标题
FREDSR：单图超级分辨率的傅里叶残留效率扩散GAN
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution
论文作者
论文摘要
FredSR是一种GAN变体，旨在在特定任务中胜过传统的GAN模型，例如单图超级分辨率，具有极端参数效率，以每数据库的通用性成本。 FREDSR集成了快速的傅立叶变换，剩余预测，扩散歧视器等，以在与仅360p的单个图像3X超级分辨率的UHDSR4K数据集中的其他模型相比，仅360p和720p，只有37000参数。该模型遵循给定数据集的特征，从而导致较低的概括性，但在诸如实时上缩放之类的任务上的性能较高。
FREDSR is a GAN variant that aims to outperform traditional GAN models in specific tasks such as Single Image Super Resolution with extreme parameter efficiency at the cost of per-dataset generalizeability. FREDSR integrates fast Fourier transformation, residual prediction, diffusive discriminators, etc to achieve strong performance in comparisons to other models on the UHDSR4K dataset for Single Image 3x Super Resolution from 360p and 720p with only 37000 parameters. The model follows the characteristics of the given dataset, resulting in lower generalizeability but higher performance on tasks such as real time up-scaling.
