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Erschienen in:
Deep Graph Laplacian Hashing for Image Retrieval Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Single Image Dehazing Using Deep Convolution Neural Networks

verfasst von : Shengdong Zhang, Fazhi He, Jian Yao

Erschienen in: Advances in Multimedia Information Processing – PCM 2017

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Single Image Haze Removal Based on Global-Local Optimization for Depth Map
Nächstes Kapitel SPOS: Deblur Image by Using Sparsity Prior and Outlier Suppression
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Metadaten
Titel
Single Image Dehazing Using Deep Convolution Neural Networks
verfasst von
Shengdong Zhang
Fazhi He
Jian Yao
Copyright-Jahr
2018
Buch
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77379-7

Electronic ISBN: 978-3-319-77380-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77380-3_13

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