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.
Abstract (translated)
尽管已经存在许多图像超分辨率解决方案，但它们仍然面临许多挑战。基于卷积神经网络（CNN）的算法，尽管在计算复杂性方面有所降低，但仍需要提高其准确性。而基于Transformer的算法具有更高的准确性，但它们的超高计算复杂性使得它们难以在实际应用中接受。为了克服现有挑战，本文提出了一种新颖的超分辨率重构算法。该算法通过独特的设计在保持低复杂性的同时实现了显著的准确率增加。算法的核心在于其巧妙设计的全局局部信息提取模块和基本模块。通过结合全局和局部信息，全局局部信息提取模块旨在更全面地理解图像内容，从而更准确地恢复图像的全局结构和局部细节，为后续的重建过程提供了丰富的信息支持。实验结果表明，本文提出的算法的全面性能最优，为超分辨率重构领域提供了一种高效且实用的全新解决方案。
URL
https://arxiv.org/abs/2405.01085
