  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.
