Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution
This work addresses efficiency challenges in image super-resolution for applications requiring lightweight models, though it is incremental as it builds on existing transformer and convolution designs.
The authors tackled the problem of high computational and memory costs in lightweight image super-resolution by proposing a neighborhood attention module and an enhanced feed-forward network, resulting in a model that outperforms existing lightweight methods and achieves state-of-the-art performance.
In recent years, the use of large convolutional kernels has become popular in designing convolutional neural networks due to their ability to capture long-range dependencies and provide large receptive fields. However, the increase in kernel size also leads to a quadratic growth in the number of parameters, resulting in heavy computation and memory requirements. To address this challenge, we propose a neighborhood attention (NA) module that upgrades the standard convolution with a self-attention mechanism. The NA module efficiently extracts long-range dependencies in a sliding window pattern, thereby achieving similar performance to large convolutional kernels but with fewer parameters. Building upon the NA module, we propose a lightweight single image super-resolution (SISR) network named TCSR. Additionally, we introduce an enhanced feed-forward network (EFFN) in TCSR to improve the SISR performance. EFFN employs a parameter-free spatial-shift operation for efficient feature aggregation. Our extensive experiments and ablation studies demonstrate that TCSR outperforms existing lightweight SISR methods and achieves state-of-the-art performance. Our codes are available at \url{https://github.com/Aitical/TCSR}.