Climate Downscaling of Image Super-Resolution Based on Implicit Neural Representation

High-resolution climate data is crucial for local and regional-scale production and livelihoods. Deep learning-based downscaling techniques can effectively bridge the gap between existing low-resolution climate data and application requirements. Deep learning-based downscaling methods that can generate high-resolution climate data are important for both local and regional production activities. However, the existing methods are often constrained by fixed scaling factors, which lead to high training costs in multiscale scenarios. However, their results for climate data are usually blurred and inaccurate in terms of high-frequency details. To address these limitations, this study proposes a deep learning super-resolution network that fuses implicit neural representations and adaptive feature encoding for arbitrary-scale climate downscaling. This method designs a dynamic pixel feature aggregation module to dynamically adjust the feature-encoding process using a learnable modulator, which can adapt to different scaling factors. Additionally, the implicit neural representation of the images is designed to predict continuous-domain pixel values by fusing coordinate linear difference features and neighborhood nonlinear features via an attention mechanism. Finally, combined with a high-order degradation training strategy, experiments on the ECMMWF HRES and ERA5 datasets demonstrate that the proposed method achieves a Peak Signal to Noise Ratio (PSNR) improvement of at least 0.7 dB at 2× scaling factor compared to fixed-ratio methods and outperforms existing arbitrary-ratio methods by at least 0.48 dB under the same scaling condition. These quantitative results demonstrate that the proposed approach is superior to existing methods because it provides a more flexible and efficient solution for meteorological data processing.