<![CDATA[Computational resource limitations, particularly the use of CPU-based systems without GPU acceleration, require super-resolution models that can achieve a balance between reconstruction quality and computational efficiency. This study proposes a lightweight hybrid super-resolution model designed for resource-constrained computing environments. The proposed architecture integrates lightweight feature extraction, residual learning, and an efficient image reconstruction module to enhance image resolution while maintaining low computational complexity. Model performance is evaluated using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), computation time, CPU utilization, and memory consumption under different scaling factors. Experimental results show that the proposed model achieves competitive reconstruction performance while maintaining computational efficiency compared with several lightweight benchmark models. These findings indicate that the proposed hybrid architecture is suitable for deployment in resource-limited computing environments. Future work may focus on evaluating and optimizing the model on edge computing platforms, including smartphones and embedded systems, to validate its performance under real-world operating conditions.]]>
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