GCEPANet: A lightweight and efficient remote sensing image cloud removal network model for optical-SAR image fusion

• GCEPANet reduces model complexity and the number of parameters while improving accuracy. • The GCEPA module enhances image reconstruction quality by leveraging both spatial and spectral dimensions. • The joint loss function is more effective in preserving spectral and structural information. To mitigate severe cloud interference in optical remote sensing imagery and address the challenges of deploying complex cloud removal models on satellite platforms, this study proposes a lightweight gated parallel attention network, GCEPANet. By integrating optical and SAR data, the network fully exploits the penetration capability of SAR imagery and combines a Gated Convolution Module (GCONV) with an Enhanced Parallel Attention Module (EPA) to establish a “cloud perception–cloud refinement” cooperative mechanism. This mechanism enables the model to identify and filter features according to cloud intensity, effectively separating the feature flows of clear and cloudy regions, and adaptively compensating for cloud-induced degradation to reconstruct the true structural and radiative characteristics of surface objects. Furthermore, a joint spectral–structural loss is introduced to simultaneously constrain spectral consistency and structural fidelity. Extensive experiments on the SEN12MS-CR dataset demonstrate that the proposed GCEPANet consistently outperforms existing methods across multiple metrics, including PSNR, SSIM, MAE, RMSE, SAM, and ERGAS. Compared with the SCTCR model, GCEPANet achieves a 0.9306 dB improvement in PSNR, reduces the number of parameters by 85.5% (to 12.77M), and decreases FLOPs by 76.0% (to 9.71G). These results demonstrate that the proposed method achieves superior cloud removal performance while significantly reducing model complexity, providing an efficient and practical solution for real-time on-orbit cloud removal in optical–SAR fused remote sensing imagery.