For the problem of degrading and blurring in remote sensing images, the classical image restoration methods have poor restoration effect due to the difficulty of estimating the blur function. In order to avoid the difficulty of estimating the blur function, the authors have studied the image restoration method based on Conditional Generative Adversarial Nets (CGAN) through depth learning. Firstly, the training database of the training network is created, and then the initial parameters of the training network are set. The network alternately learns the generator model and the discriminator model in the adversarial way. By learning the difference between the degraded image and the clear image continuously and combining the adversarial loss with the perceptual loss, the difference between them can be reduced and the image restoration can be realized. A Hybrid blur training library based on GOPRO data set is used to train the network, and is compared with other methods. The results show that this means has better restoration effect in image details and evaluation indexes. The details and texture information of the restored image are guaranteed, and the method of conditional generation antagonism network is proved to be applicable to the restoration of remote sensing image.
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