Enhancing Solar Continuum Resolution Using SDO/HMI-ONSET Data

High-resolution solar observations are critical for resolving small-scale dynamic processes in the Sun. Specifically, solar continuum observations, which characterize photospheric radiative energy distribution, diagnose atmospheric temperature gradients, and model space weather events, serve as a cornerstone of solar physics research. However, existing observational frameworks face inherent limitations: space-based instruments are constrained by diffraction limits, while ground-based data suffer from atmospheric turbulence and temporal discontinuity. To address these challenges, this study proposes a resolution enhancement method based on cross-platform data fusion between SDO/HMI (space-based full-disk coverage) and ONSET (ground-based high-resolution local observations), aiming to overcome the physical limitations of single-instrument observations. Utilizing 6,537 preprocessed spatiotemporally aligned datasets (2022 years), we achieve sub-pixel registration via the SIFT algorithm and design a lightweight model named CISR (Cross-Instrument Super-Resolution) based on a Residual Local Feature Block network, optimized for feature extraction and reconstruction using the SmoothL1 loss function. Experimental results demonstrate that CISR achieves CC of 0.946, PSNR of 33.924 dB, and SSIM of 0.855 on the test set, significantly outperforming bicubic interpolation and the SRCNN baseline model. Qualitative visual assessment verifies the method's efficacy in HMI continuum data resolution enhancement, with exceptional performance in maintaining both sunspot boundary acuity and granule structural fidelity. This work provides a novel approach for multi-source solar data synergy, with future potential to incorporate physics-driven evaluation metrics for further improving model generalization.