A hybrid features learning model for single image haze prediction
Signal, Image and Video Processing(2018)
摘要
In scene dehazing problem, single image haze prediction is one of the most challenging issues. In this paper, we propose a hybrid features learning model (HFLM) for haze prediction. HFLM takes a hazy image as the input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. There are two main stages in HFLM. The first stage is used to extract haze-related features from haze images. The second stage aims to establish the mapping relationship between features and medium transmission. In the experimental part, we explore the hyper-parameters in support vector and verify the significance of the features selection. Further, we compare our method with other dehazing methods and make a qualitative comparison on synthetic images. Demonstrate our method has more superior performance than the state-of-the-art dehazing methods.
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关键词
Medium transmission, Support vector regression, Haze-related features, Visual perception
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