论文标题
Koalanet：使用面向内核的自适应局部调整的盲级超分辨率
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment
论文作者
论文摘要
盲目的超分辨率（SR）方法旨在从包含未知降解的低分辨率图像中产生高质量的高分辨率图像。但是，自然图像包含各种类型和数量的模糊：有些可能是由于摄像机的固有降解特性所致，但有些甚至可能是故意的，出于美学目的（例如，散景效应）。在后者的情况下，SR方法很难将模糊删除，而将其视为IS。在本文中，我们提出了一个新型的盲目SR框架，基于以内核为导向的SR特征的核心自适应局部调整（Koala），称为Koalanet，该特征共同学习了空间变化的降解和恢复核，以适应实际图像中空间var的模糊特征。我们的Koalanet的表现优于最近通过随机降解获得的合成LR图像的近期盲SR方法，我们进一步表明，提议的Koalanet对具有故意模糊的艺术照片产生了最自然的结果，而这些模糊没有过度塑造，而不是通过与内部和非核心区域混合的有效处理图像进行有效处理图像。
Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations. However, natural images contain various types and amounts of blur: some may be due to the inherent degradation characteristics of the camera, but some may even be intentional, for aesthetic purposes (e.g. Bokeh effect). In the case of the latter, it becomes highly difficult for SR methods to disentangle the blur to remove, and that to leave as is. In this paper, we propose a novel blind SR framework based on kernel-oriented adaptive local adjustment (KOALA) of SR features, called KOALAnet, which jointly learns spatially-variant degradation and restoration kernels in order to adapt to the spatially-variant blur characteristics in real images. Our KOALAnet outperforms recent blind SR methods for synthesized LR images obtained with randomized degradations, and we further show that the proposed KOALAnet produces the most natural results for artistic photographs with intentional blur, which are not over-sharpened, by effectively handling images mixed with in-focus and out-of-focus areas.
