To address the prevalent focus on reducing the parameter counts in current efficient super-resolution reconstruction algorithms, this study introduces an innovative efficient global attention network to solve the issues regarding neglecting hierarchical features and the underutilization of high-dimensional image features. The core concept of the network involves implementing cross-adaptive feature blocks for deep feature extraction at varying image levels to remove the insufficiency in high-frequency detail information of images. To enhance the reconstruction of edge detail information, a nearest-neighbor pixel reconstruction block was constructed by merging spatial correlation with pixel analysis to further promote the reconstruction of edge detail information. Moreover, a multistage dynamic cosine thermal restart training strategy was introduced. This strategy bolsters the stability of the training process and refines network performance through dynamic learning rate adjustments, mitigating model overfitting. Exhaustive experiments demonstrate that when the proposed method is tested against five benchmark datasets, including Set 5, it increases the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) performance metrics by an average of 0.51 dB and 0.0078, respectively, and trims the number of parameters and floating-point operations (FLOPs) by an average of 332×10³ and 70×10⁹ compared with leading networks. In conclusion, the proposed method not only reduces complexity but also excels in performance metrics and visualization, thereby attaining remarkable network efficiency.