Unsupervised single-image dehazing using the multiple-scattering model
An unsupervised single-image dehazing method using a multiple scattering model is proposed. The method uses an undegraded atmospheric multiple scattering model and unsupervised learning to implement dehazing on single real-world image. The atmospheric multiple scattering model can avoid the influenc...
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Veröffentlicht in: | Applied optics (2004) 2021-09, Vol.60 (26), p.7858-7868 |
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Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | An unsupervised single-image dehazing method using a multiple
scattering model is proposed. The method uses an undegraded
atmospheric multiple scattering model and unsupervised learning to
implement dehazing on single real-world image. The atmospheric
multiple scattering model can avoid the influence of multiple
scattering on the image and the unsupervised neural network can avoid
the intensive operation on the data set. In this method, three
unsupervised learning branches and a blur kernel estimation module
estimate the scene radiation layer, transmission layer, atmospheric
light layer, and blur kernel layer, respectively. In addition, the
unsupervised loss function is constructed by prior knowledge to
constrain the unsupervised branches. Finally, the output of the three
unsupervised branches and the blur kernel estimation module
synthesizes the haze image in a self-supervised way. A large number of
experiments show that the proposed method has good performance in
image dehazing compared with the six most advanced dehazing
methods. |
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ISSN: | 1559-128X 2155-3165 1539-4522 |
DOI: | 10.1364/AO.426651 |