Abstract
Haze removal is urgently desired in multi-media system. A deep learning-based method, called dehazingCNN, is proposed to estimate an approximate clear image. The proposed learning model is different from traditional learning based method. We adopts Deep Convolution Neural Networks (CNN) to take a hazy image as the input and outputs the corresponding clear image directly. The output of the network is high quality except some block artifacts and color distortions. We can remove the color distortion in the approximate clear image via atmospheric scattering model and guided filter effectively. Experimental results on different type of images, such as synthetic and benchmark of hazy images, demonstrate that the proposed method is comparative to and even better than many complex state-of-the-art methods.
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Acknowledgement
National Science Foundation of China (Grant No. 61472289) and National Key Research and Development Project of China (Grant No. 2016YFC0106305).
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Zhang, S., He, F., Yao, J. (2018). Single Image Dehazing Using Deep Convolution Neural Networks. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_13
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