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

Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration