Single Image Super-Resolution Based on Global-Local Information Synergy

TL;DR

This paper introduces a novel super-resolution algorithm that effectively combines global and local image information, achieving high accuracy with low computational complexity for practical applications.

Contribution

The paper proposes a unique Global-Local Information Extraction Module and Basic Block Module to enhance super-resolution accuracy while maintaining low complexity.

Findings

Achieves optimal comprehensive performance in super-resolution tasks.

Effectively combines global and local information for better image reconstruction.

Provides an efficient and practical solution for super-resolution reconstruction.

Abstract

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.