An improved generative adversarial networks for remote sensing image super-resolution reconstruction via multi-scale residual block

Existing image super-resolution algorithms still suffer from the problems of not extracting rich image features and losing realistic high-frequency details. In order to solve these problems, this paper proposes an improved generative adversarial network algorithm for super-resolution reconstruction of remote sensing images by multi-scale residual blocks. The original generative adversarial network (GAN) structure is improved and multi-scale residual blocks are introduced in the generator to fuse features at different scales. After extracting the parallel information of multi-scale features, information is exchanged between multi-resolution information streams to obtain contextual information through spatial and channel attention mechanisms, and multi-scale features are fused according to the attention mechanism. In the discriminator, the concept of relative average GAN (RaGAN) is introduced, and the loss function of the network is redesigned so that the discriminator can predict relative probabilities instead of absolute probabilities thus enabling clear learning of edge and texture details. Experimental results show that the proposed method in this paper significantly outperforms state-of-the-art (SOTA) methods in terms of both subjective and objective metrics.In three test datasets, compared with SOTA methods, the Peak Signal to Noise Ratio(PSNR) is improved by a maximum of 1.18 dB, 0.84 dB and 1.29 dB respectively, and the Structural Similarity Index (SSIM) is improved by 0.0264, 0.0077 and 0.0109 respectively in scale of 2, 3 and 4 times images super-resolution.The model proposed in this paper effectively improved the super-resolution re-construction results of remote sensing images.