高效全局注意网络的图像超分辨率重建

Abstract

To address the prevalent focus on reducing the parameter counts in current efficient superresolution reconstruction algorithms, this study introduces an innovative efficient global attention network to solve the issues regarding neglecting hierarchical features and the underutilization of highdimensional image features. The core concept of the network involves implementing crossadaptive feature blocks for deep feature extraction at varying image levels to remove the insufficiency in highfrequency detail information of images. To enhance the reconstruction of edge detail information, a nearestneighbor 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 signaltonoise 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 floatingpoint operations (FLOPs) by an average of 332× 10 3 and 70×10 9 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.