Research on Remote Sensing Image De‐haze Based on GAN


Commonly used remote sensing image de-haze methods include: the image enhancement method and a physical model-based. However, when the above methods are applied to high-resolution remote sensing images, problems with texture information loss and insufficient enhancement often occur. These problems affect further analysis and application of high-resolution remote sensing images. This paper proposes a new single-image de-haze method called texture attention GAN. In this network, in order to solve the problem of texture information loss in the process of de-haze, a texture attention-based generator is adopted. When design the network discriminator, the global and local discriminators are used to improve the distortion of image details. In comparison with several common methods, this method has achieved better results.

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This study was funded by [Doctoral Research Fund Project of Heilongjiang Institute of Technology (2017BJ15), Heilongjiang Science Foundation Project (LH2020F047), Heilongjiang Institute of Technology Innovation Team Project (2020CX07)]

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Correspondence to Xianhong Zhang.

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Zhang, X. Research on Remote Sensing Image De‐haze Based on GAN. J Sign Process Syst (2021).

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  • Machine learning
  • Deep learning
  • GAN
  • De‐haze