  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.
