In practical application, the performance of recognition network usually decreases when being applied on super-resolution images. In this paper, we propose a feature-based recognition network combined with GAN (FGAN). Our network improves the recognition accuracy by extracting more features that benefit recognition from SR images. In the experiment, we build three datasets using three different super-resolution algorithm, and our network increases the recognition accuracy by more than 6% comparing with ReaNet50 and DenseNet121.

翻译：在实际应用中,在应用超分辨率图像时,识别网络的性能通常会下降。在本文中,我们提议建立一个基于地貌的识别网络,与GAN(FGAN)相结合。我们的网络通过提取更多能从SR图像中识别的特征来提高识别准确性。在实验中,我们用三种不同的超级分辨率算法建立了三个数据集,我们的网络将识别准确性比ReaNet50和DenseNet121提高了6%以上。