Multi-Axis Feature Diversity Enhancement for Remote Sensing Video Super-Resolution

Abstract

How to aggregate spatial-temporal information plays an essential role in video super-resolution (VSR) tasks. Despite the remarkable success, existing methods adopt static convolution to encode spatial-temporal information, which lacks flexibility in aggregating information in large-scale remote sensing scenes, as they often contain heterogeneous features (e.g., diverse textures). In this paper, we propose a spatial feature diversity enhancement module (SDE) and channel diversity enhancement module (CDE), which explore the diverse representation of different local patterns while aggregating the global response with compactly channel-wise embedding representation. Specifically, SDE introduces multiple learnable filters to extract representative spatial variants and encodes them to generate a dynamic kernel for enriched spatial representation. To explore the diversity in the channel dimension, CDE exploits the discrete cosine transform to transform the feature into the frequency domain. This enriches the channel representation while mitigating massive frequency loss caused by pooling operation. Based on SDE and CDE, we further devise a multi-axis feature diversity enhancement (MADE) module to harmonize the spatial, channel, and pixel-wise features for diverse feature fusion. These elaborate strategies form a novel network for satellite VSR, termed MADNet, which achieves favorable performance against state-of-the-art method BasicVSR++ in terms of average PSNR by 0.14 dB on various video satellites, including JiLin-1, Carbonite-2, SkySat-1, and UrtheCast. Code will be available at https://github.com/XY-boy/MADNet