Recent research on image restoration have achieved great success with the aid of deep learning technologies, but, many of them are limited to dealing SR with realistic settings. To alleviate this problem, we introduce a new formulation for image super-resolution to solve arbitrary scale image super-resolution methods. Based on the proposed new SR formulation, we can not only super-resolve images with multiple scales, but also find a new way to analyze the performance of super-resolving process. We demonstrate that the proposed method can generate high-quality images unlike conventional SR methods.
翻译:最近关于图像恢复的研究在深层学习技术的帮助下取得了巨大成功,但其中许多研究仅限于用现实的设置处理SR。为了缓解这一问题,我们引入了图像超分辨率新配方,以解决任意的图像超分辨率超分辨率方法。根据拟议的新的SR配方,我们不仅可以找到具有多个尺度的超级分辨率图像,还可以找到分析超分辨率进程绩效的新方法。我们证明,拟议的方法可以产生与传统的SR方法不同的高质量图像。