Single Image Super-Resolution Based on Global-Local Information Synergy
This work addresses the problem of efficient and accurate super-resolution for practical applications, offering an incremental improvement over existing CNN and Transformer-based methods.
The paper tackles the challenge of balancing accuracy and computational complexity in single image super-resolution by proposing a novel algorithm that combines global and local information extraction, achieving optimal comprehensive performance with a significant increase in accuracy while maintaining low complexity.
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.