Remote Sensing Image Super-Resolution via Multiscale Enhancement Network

Abstract

In recent years, remote sensing images have attracted a lot of attention because of their special value. However, images acquired by satellite sensors are usually low-resolution (LR), so remote sensing images are much more difficult to infer high-frequency details from compared with ordinary digital images, which means they cannot meet the needs of certain downstream tasks. In this letter, we propose a multiscale enhancement network (MEN), which uses multiscale features of remote sensing images to enhance the network's reconstruction capability. Specifically, the network extracts the coarse features of LR remote sensing images using convolutional layers. Then, these features are fed into the multiscale enhancement module (MEM) proposed by this network, which uses a combination of convolutional layers with multiple convolutional kernel sizes to refine the extraction of multiscale features, and finally, the final reconstructed image is generated by the reconstruction module. Extensive experiments show that MEN achieves significant reconstruction advantages in both objective and subjective aspects.