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Single Traffic Image Deraining via Similarity-Diversity Model
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NETL
NSTL
IEEE
Single traffic image deraining technology based on deep learning is a vital branch of image preprocessing, which is of great help to intelligent monitoring systems and driving navigation system. It is well understood that established deraining methods are derived based on one specific imaging model, neglecting the underlying correlations between different weather models and thereby limiting the applicability of these standard methods in real scenarios. To ameliorate this issue, in this work, we first explore the inherent relationship between a rain model and the haze one established up to date. We discover that these two models experience similar degradations in the low-frequency components (i.e., similarity) but diverse degradations in the high-frequency areas (i.e., diversity). Based on these observations, we develop a Similarity-Diversity model to describe these characteristics. Afterwards, we introduce a novel deep neural network to restore the rain-free background embedding the similarity-diversity model, namely deep similarity-diversity network (DSDNet). Extensive experiments have been conducted to evaluate our proposed method that outperforms the other state of the art deraining techniques. On the other hand, we deploy the proposed algorithm with Google Vision API for object recognition, which also obtains satisfactory results both qualitatively and quantitatively.
RainDegradationImage restorationAtmospheric modelingCorrelationTask analysisImage color analysis
Youxing Li、Rushi Lan、Huiwen Huang、Huiyu Zhou、Zhenbing Liu、Cheng Pang、Xiaonan Luo
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Guangxi Key Laboratory of Intelligent Processing of Computer Image and Graphics, Guilin University of Electronic Technology, Guilin, China
School of Informatics, University of Leicester, Leicester, U.K
National Local Joint Engineering Research Center of Satellite Navigation and Location Service, Guilin University of Electronic Technology, Guilin, China
NETL
NSTL
IEEE
