Digital Surface Model and Fractal-Guided Multi-Directional Network for Remote Sensing Image Super-Resolution

As an economical and effective method to enhance the resolution of remote sensing images (RSIs), remote sensing image super-resolution (RSISR) has been widely studied. However, the existing methods lack the utilization of prior information in RSIs, which leads to unsatisfactory detail representation in the reconstructed images. To address this, in this paper, we propose a digital surface model (DSM) and fractal-guided multi-directional super resolution network (DFMDN), which utilizes additional explicit priors from DSM to facilitate the reconstruction of realistic high-frequency details. Meanwhile, to more accurately identify relationships between objects in RSIs, we design a multi-directional feature extraction module: multi-directional residual-in-residual dense blocks (MDRRDB), which captures the variation from different viewing angles. Finally, to guide and constrain the network to generate reconstructed images with textures that align more closely with natural patterns, we develop a fractal mapping algorithm (FMA) and a related loss function. Our method demonstrates significant improvements in both quantitative metrics and visual quality compared to existing approaches on various datasets.