MMDAN: multiwavelet based multiscale dilated attention network for remote sensing image super-resolution

Restoring a high-resolution (HR) image from a low-resolution (LR) image using deep learning (DL) techniques is becoming a popular restoration approach in the remote sensing image super-resolution (SR). However, blurry object edges, artifacts, memory usage, and computational burdens are still challenges in remote sensing image SR. To overcome these challenges, a lightweight Multiwavelet-based Multiscale Dilated Attention Network (MMDAN) for remote-sensing image SR is proposed. The main aim of the proposed work is to reconstruct the HR image in the multiwavelet domain. The SR scheme based on the multiwavelets is proposed under a DL framework to exploit the contextual information from sixteen subbands of multiwavelets. A multiscale dilated convolution, along with a nested attention module, is employed as a deep feature extraction function to enhance the image restoration of the proposed model. Experiments on remote sensing and natural image datasets show the superiority of the proposed model in resolution enhancement.