While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e.g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data. In this paper we present a novel model for ultra high-resolution image restoration, referred to as the Global-Local Stepwise Generative Network (GLSGN), which employs a stepwise restoring strategy involving four restoring pathways: three local pathways and one global pathway. The local pathways focus on conducting image restoration in a fine-grained manner over local but high-resolution image patches, while the global pathway performs image restoration coarsely on the scale-down but intact image to provide cues for the local pathways in a global view including semantics and noise patterns. To smooth the mutual collaboration between these four pathways, our GLSGN is designed to ensure the inter-pathway consistency in four aspects in terms of low-level content, perceptual attention, restoring intensity and high-level semantics, respectively. As another major contribution of this work, we also introduce the first ultra high-resolution dataset to date for both reflection removal and rain streak removal, comprising 4,670 real-world and synthetic images. Extensive experiments across three typical tasks for image background restoration, including image reflection removal, image rain streak removal and image dehazing, show that our GLSGN consistently outperforms state-of-the-art methods.

翻译：尽管常规尺寸退化图像的背景恢复研究已取得显著进展，但由于计算复杂度与内存占用的爆炸式增长以及标注数据的匮乏，超高清（如4K）图像的修复仍是一项极具挑战性的任务。本文提出一种名为全局-局部渐进式生成网络（GLSGN）的新型超高清图像修复模型，该模型采用包含四条修复路径的渐进式修复策略：三条局部路径与一条全局路径。局部路径专注于在高分辨率局部图像块上进行细粒度图像修复，而全局路径则在缩小的完整图像上进行粗粒度修复，为局部路径提供全局层面的语义与噪声模式等线索。为平滑四条路径间的协同合作，GLSGN从低级内容、感知注意力、修复强度与高级语义四个维度确保路径间一致性。作为本研究的另一主要贡献，我们首次引入了适用于反射去除与雨痕消除的超高清数据集，包含4,670张真实与合成图像。在图像反射去除、雨痕消除及去雾三类典型背景恢复任务上的大量实验表明，GLSGN consistently优于现有最优方法。