深度学习图像去雾比较和分析 Deep learning for dehazing: Comparison and analysis

我们将基于深度学习的最新去雾方法DehazeNet与传统的最先进方法进行比较,并参考基准数据。DehazeNet根据单色图像上的透射因子估算深度图,该图用于在存在雾度时反转Koschmieder成像模型。从这个意义上说,解决方案仍然依附于Koschmieder模型。我们证明了网络对透射的估计很好,但是由于使用相同的成像模型,这种方法也显示出与其他方法相同的局限性。

We compare a recent dehazing method based on deep learning, Dehazenet, with traditional state-of-the-art approaches, on benchmark data with reference. Dehazenet estimates the depth map from transmission factor on a single color image, which is used to inverse the Koschmieder model of imaging in the presence of haze. In this sense, the solution is still attached to the Koschmieder model. We demonstrate that the transmission is very well estimated by the network, but also that this method exhibits the same limitation than others due to the use of the same imaging model.

https://arxiv.org/abs/1806.10923v1

DehazeNet官方Matlab代码实现:

https://github.com/caibolun/DehazeNet

DehazeNet Python 代码实现:

https://github.com/zlinker/DehazeNet

转载请注明:《深度学习图像去雾比较和分析 Deep learning for dehazing: Comparison and analysis

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