Gradual Network for Single Image De-raining
| Autor: | Litong Feng, Nong Xiao, Wayne Zhang, Huang Zhe, Weijiang Yu |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Artificial Intelligence Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Process (computing) Mist 02 engineering and technology Texture (music) Residual Sample (graphics) Image (mathematics) Multimedia (cs.MM) Artificial Intelligence (cs.AI) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Stage (hydrology) Artificial intelligence Single image business Computer Science - Multimedia Block (data storage) |
| Zdroj: | ACM Multimedia |
| DOI: | 10.48550/arxiv.1909.09677 |
| Popis: | Most advances in single image de-raining meet a key challenge, which is removing rain streaks with different scales and shapes while preserving image details. Existing single image de-raining approaches treat rain-streak removal as a process of pixel-wise regression directly. However, they are lacking in mining the balance between over-de-raining (e.g. removing texture details in rain-free regions) and under-de-raining (e.g. leaving rain streaks). In this paper, we firstly propose a coarse-to-fine network called Gradual Network (GraNet) consisting of coarse stage and fine stage for delving into single image de-raining with different granularities. Specifically, to reveal coarse-grained rain-streak characteristics (e.g. long and thick rain streaks/raindrops), we propose a coarse stage by utilizing local-global spatial dependencies via a local-global subnetwork composed of region-aware blocks. Taking the residual result (the coarse de-rained result) between the rainy image sample (i.e. the input data) and the output of coarse stage (i.e. the learnt rain mask) as input, the fine stage continues to de-rain by removing the fine-grained rain streaks (e.g. light rain streaks and water mist) to get a rain-free and well-reconstructed output image via a unified contextual merging sub-network with dense blocks and a merging block. Solid and comprehensive experiments on synthetic and real data demonstrate that our GraNet can significantly outperform the state-of-the-art methods by removing rain streaks with various densities, scales and shapes while keeping the image details of rain-free regions well-preserved. Comment: In Proceedings of the 27th ACM International Conference on Multimedia (MM 2019) |
| Databáze: | OpenAIRE |
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