Although several image super-resolution solutions exist, they still face many challenges. CNN-based algorithms, despite the reduction in computational complexity, still need to improve their accuracy. While Transformer-based algorithms have higher accuracy, their ultra-high computational complexity makes them difficult to be accepted in practical applications. To overcome the existing challenges, a novel super-resolution reconstruction algorithm is proposed in this paper. The algorithm achieves a significant increase in accuracy through a unique design while maintaining a low complexity. The core of the algorithm lies in its cleverly designed Global-Local Information Extraction Module and Basic Block Module. By combining global and local information, the Global-Local Information Extraction Module aims to understand the image content more comprehensively so as to recover the global structure and local details in the image more accurately, which provides rich information support for the subsequent reconstruction process. Experimental results show that the comprehensive performance of the algorithm proposed in this paper is optimal, providing an efficient and practical new solution in the field of super-resolution reconstruction.

翻译：尽管现有的图像超分辨率解决方案多种多样，但仍面临诸多挑战。基于CNN的算法虽然降低了计算复杂度，但精度仍有待提升。而基于Transformer的算法尽管精度更高，但其极高的计算复杂度使其在实际应用中难以被接受。为克服现有挑战，本文提出了一种新型超分辨率重建算法。该算法在保持低复杂度的同时，通过独特设计实现了精度的显著提升。算法核心在于巧妙设计的全局-局部信息提取模块和基础块模块。全局-局部信息提取模块通过融合全局与局部信息，旨在更全面地理解图像内容，从而更准确地恢复图像中的全局结构与局部细节，为后续重建过程提供丰富的信息支持。实验结果表明，本文提出的算法综合性能最优，为超分辨率重建领域提供了一种高效实用的新方案。