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- W3086800588 abstract "Rain streaks severely degenerate the performances of image/video processing tasks, therefore effective methods for removing rain streaks are required for a wide range of practical applications. In this paper, we introduce an end-to-end deep network, called GridDerainNet, to remove rain streaks within single image under different conditions. The architecture of GridDerainNet consists of three modules: pre-processing, multi-scale attentive module and post-processing. The pre-processing module can effectively generate several variants of the given rainy image, in order to extract more key features from the input. The multi-scale attentive module implements a novel attention mechanism, which allows more flexible information exchange and aggregation, taking full use of diversities of a given image. In the end, post-processing module furthers to reduce residual artifacts after previous two steps. Quantitative and qualitative experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art methods on both synthetic and real-world images." @default.
- W3086800588 created "2020-09-21" @default.
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- W3086800588 date "2021-01-01" @default.
- W3086800588 modified "2023-10-17" @default.
- W3086800588 title "Single image rain removal via multi-module deep grid network" @default.
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- W3086800588 doi "https://doi.org/10.1016/j.cviu.2020.103106" @default.
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