Research on Super-resolution Reconstruction Algorithm of Remote Sensing Image Based on Generative Adversarial Networks
Jiang Wenjie, Luo Xiaoshu · 2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) · 2019
Due to natural conditions and hardware manufacturing processes, the resolution of remote sensing images is generally low. Obtaining high-definition remote sensing images by simply improving hardware and manufacturing processes is not only costly and technically challenging but also cannot be deployed on a large scale. Aiming at the limitations of the traditional methods, this paper studies the image super-resolution reconstruction method for improving the generated anti-network. Firstly, the generator network is optimized, and an RRDB (Residual-in-Residual Dense without BN (Batch Normalization) is used. Block) module; secondly, the related idea of relativistic GAN (relativistic generative adversarial network) is introduced, the relative value of the discriminator is not the absolute value; finally, the sensation loss is improved, and the feature is used before the function is activated. The test results show that the proposed algorithm is better than SRGAN (Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network), SRCNN (Super-Resolution Convolutional Neural Network) and FSRCNN (Fast Super-Resolution Convolutional Neural Network). The clarity of the reconstructed image is improved, and the reconstructed image quality is significantly improved.