Arbitrary-Scale Planetary Remote Sensing Super-Resolution via Adaptive Frequency–Spatial Neural Operator

Planetary remote sensing super-resolution aims to enhance the spatial resolution and fine details from low-resolution images. In practice, planetary remote sensing is inherently constrained by sensor payload limitations and communication bandwidth, resulting in restricted spatial resolution and inconsistent scale factors across observations. These constraints make it impractical to acquire uniform high-resolution images, thereby motivating the need for arbitrary-scale super-resolution capable of dynamically adapting to diverse imaging conditions and mission design restrictions. Despite extensive progress in general SR, such constraints remain under-addressed in planetary remote sensing. To address those challenges, this article proposes an arbitrary-scale super-resolution (SR) model, the Adaptive Frequency–Spatial Neural Operator (AFSNO), designed to address the regional context homogeneity and heterogeneous surface features of planetary remote sensing images through frequency separation and non-local receptive field. The AFSNO integrates Frequency–Spatial Hierarchical Encoder (FSHE) and Fusion Neural Operator in a unified framework, achieving arbitrary-scale SR tailored for planetary image characteristics. To evaluate the performance of AFSNO in planetary remote sensing, we introduce Ceres-1K, the planetary remote sensing dataset. Experiments on Ceres-1K demonstrate that AFSNO achieves competitive performance in both objective assessment and perceptual quality while preserving fewer parameters. Beyond pixel metrics, sharper edges and high-frequency detail enable downstream planetary analyses. The lightweight, arbitrary-scale design also suits onboard processing and efficient data management for future missions.