Single image rain removal using image decomposition and a dense network
| Autor: | Wenfeng Yan, Shuzhen Chen, Xiaohua Zhang, Qiusheng Lian |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Rain removal Pattern recognition 02 engineering and technology Function (mathematics) 010501 environmental sciences 01 natural sciences Regularization (mathematics) Convolutional neural network Image (mathematics) Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Decomposition (computer science) 020201 artificial intelligence & image processing Artificial intelligence business Scale (map) Image restoration 0105 earth and related environmental sciences Information Systems |
| Zdroj: | IEEE/CAA Journal of Automatica Sinica. :1-10 |
| ISSN: | 2329-9274 2329-9266 |
| DOI: | 10.1109/jas.2019.1911441 |
| Popis: | Removing rain from a single image is a challenging task due to the absence of temporal information. Considering that a rainy image can be decomposed into the low-frequency ( LF ) and high-frequency ( HF ) components, where the coarse scale information is retained in the LF component and the rain streaks and texture correspond to the HF component, we propose a single image rain removal algorithm using image decomposition and a dense network. We design two task-driven sub-networks to estimate the LF and non-rain HF components of a rainy image. The high-frequency estimation sub-network employs a densely connected network structure, while the low-frequency sub-network uses a simple convolutional neural network ( CNN ) . We add total variation ( TV ) regularization and LF-channel fidelity terms to the loss function to optimize the two subnetworks jointly. The method then obtains de-rained output by combining the estimated LF and non-rain HF components. Extensive experiments on synthetic and real-world rainy images demonstrate that our method removes rain streaks while preserving non-rain details, and achieves superior de-raining performance both perceptually and quantitatively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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