多尺度自相似遥感图像超分辨率重建网络设计OA

The area captured in remote sensing images is generally large,so targets with similar features have a higher probability of repeating themselves in the image.In response to this characteristic,this paper proposes a multi-scale self-similar remote sensing image super-resolution reconstruction network.By introducing a global context module in the SSEM network structure to obtain the internal recursion of single scale and cross scale information within the image,and introducing a pixel attention module in the upsampling module to enhance its feature detail extraction ability.Tests on the UC Merced dataset show that the PSNR of our algorithm is 0.11dB,0.15dB,and 0.05dB higher than that of the HSENet algorithm at 2x,3x,and 4x scales,respectively;In terms of SSIM metrics,our algorithm outperforms the HSENet algorithm by 0.0058 and 0.0013 at 3x and 4x scales,respectively.