Laser & Optoelectronics Progress, Volume. 61, Issue 10, 1037006(2024)
Efficient Global Attention Networks for Image Super-Resolution Reconstruction
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.
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Qingqing Wang, Yuelan Xin, Jia Zhao, Jiang Guo, Haochen Wang. Efficient Global Attention Networks for Image Super-Resolution Reconstruction[J]. Laser & Optoelectronics Progress, 2024, 61(10): 1037006
Category: Digital Image Processing
Received: Sep. 5, 2023
Accepted: Oct. 20, 2023
Published Online: May. 9, 2024
The Author Email: Xin Yuelan (xinyue001112@163.com)
CSTR:32186.14.LOP232053