A Dynamically Weighted Framework for Adaptive Reference-Based Super-Resolution

Abstract. Satellite remote sensing is inherently constrained by a trade-off between spatial and temporal resolution. As a result, high-temporal-frequency sensors such as Geostationary Ocean Color Imager-II provide operationally valuable observations but at coarse spatial resolution. Reference-Based Super-Resolution (Ref-SR) can address this limitation by transferring high-resolution textures from an external reference image, but temporal mismatch between the target and reference images often leads to unreliable texture transfer and severe artifacts. This problem becomes more critical in extreme low-resolution (LR) settings, where structural information is already severely degraded. To address this issue, we propose the Dynamic Ref-SR Framework, which computes a pixel-wise weight map from intensity differences between the LR and reference images to selectively control reference transfer. The resulting weights promote reference use in stable regions while suppressing it in temporally inconsistent regions. The framework was validated on three backbone architectures—CNN (EDSR), Swin Transformer, and GAN—using a Sentinel-2 dataset for four-band reconstruction (RGB and NIR). Across all metrics and architectures, the proposed Ref-SR framework consistently outperformed the SISR baseline in both structural and spectral evaluations. Among the tested backbones, the GAN-based model achieved the best overall performance, with a PSNR of 35.60 dB, an SSIM of 0.92, a SAM of 2.20°, and an ERGAS of 74.71. These results demonstrate that the proposed framework can improve LR satellite imagery while reducing the risk of reference misuse under temporal mismatch.