Edge-Aware Remote Sensing Image Super-Resolution via Kolmogorov–Arnold Networks

Remote sensing image super-resolution aims to reconstruct high-quality spatial details from low-resolution observations, which is essential for land-cover interpretation, object detection, and downstream geospatial analysis. However, existing convolutional neural network (CNN)- and Transformer-based methods may struggle to preserve sharp boundaries, high-frequency textures, and structural consistency in complex remote sensing scenes. To address these issues, this paper proposes an Edge-Guided Kolmogorov–Arnold Network (EGKAN) for remote sensing image super-resolution. The proposed method integrates the adaptive nonlinear representation capability of Kolmogorov–Arnold Networks (KANs), the global context modeling ability of Transformers, and explicit edge-aware structural modeling to enhance structural reconstruction in complex scenes. Specifically, EGKAN consists of three key components: KAN-based nonlinear feature modeling, edge-aware structure modeling, and multi-scale structural consistency supervision. First, a B-spline-based KAN activation module and Feature KAN Attention are introduced to enhance feature representation through adaptive nonlinear mappings and efficient feature-wise interaction. Second, an edge-aware structure modeling framework is designed, which progressively enhances edge representation through an Edge Attention Block, Multi-direction Edge KAN Modeling, and Edge-aware KAN Refinement, supporting the reconstruction of structural boundaries and fine-grained textures. Finally, a multi-scale edge consistency loss is proposed by jointly incorporating Sobel and Laplacian gradient constraints, which enforces structural consistency and improves boundary fidelity across different spatial scales. Extensive experiments on a self-constructed Sentinel-2 dataset demonstrate that EGKAN achieves favorable quantitative performance compared with representative CNN-, GAN-, and Transformer-based methods in terms of PSNR, SSIM, and perceptual quality. The results indicate that KAN-based nonlinear modeling can contribute to high-frequency structure reconstruction when integrated with Transformer-based global modeling and explicit edge-aware components, demonstrating the potential of KAN–Transformer hybrid architectures for remote sensing image super-resolution.