Remote Sensing Image Super-Resolution Using Texture Enhancing Generative Adversarial Network

Remote Sensing Image Super-Resolution Using Texture Enhancing Generative Adversarial Network

Single image super-resolution (SISR) brings excellent improvement in remote sensing applications, which has been widely studied in recent years. A method named TFSRGAN of remote sensing single image super-resolution based on generative adversarial network is proposed in this paper to address the problems of poor reconstruction visual quality and smooth details in traditional algorithms. In the proposed framework, s dense residual connection method is proposed to fuse the deep features from each residual block based on the SRGAN network, and the channel attention mechanism is added into the residual block to combinate the channel information. In addition, the network employs an edge extractor to divide the low-resolution image into low-frequency image and high-frequency image as the input of generator to improve the effect of texture reconstruction. Extensive comparison experiments were performed using AID, UCAS_AOD and China Gaofen-1 datasets, the SR results demonstrate that the proposed TFSRGAN framework outperforms the state-of-the-art algorithms including VDSR, SRGAN and ESRGAN in terms of objective evaluation metrics and subjective visual perception. The ground targets detection experiments represent that the proposed TFSRGAN can significantly improve the effect in remote sensing super-resolution application.