Image Super-Resolution using Efficient Striped Window Transformer
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Recently, transformer-based methods have made impressive progress in single-image super-resolu-tion (SR). However, these methods are difficult to apply to lightweight SR (LSR) due to the challenge of balancing model performance and complexity. In this paper, we propose an efficient striped window transformer (ESWT). ESWT consists of efficient transformation layers (ETLs), allowing a clean structure and avoiding redundant operations. Moreover, we designed a striped window mechanism to obtain a more efficient ESWT in modeling long-term dependencies. To further exploit the potential of the transformer, we propose a novel flexible window training strategy. Without any additional cost, this strategy can further improve the performance of ESWT. Extensive experiments show that the proposed method outperforms state-of-the-art transformer-based LSR methods with fewer parameters, faster inference, smaller FLOPs, and less memory consumption, achieving a better trade-off between model performance and complexity.
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